---
_id: '14425'
abstract:
- lang: eng
text: 'Water adsorption and dissociation processes on pristine low-index TiO2 interfaces
are important but poorly understood outside the well-studied anatase (101) and
rutile (110). To understand these, we construct three sets of machine learning
potentials that are simultaneously applicable to various TiO2 surfaces, based
on three density-functional-theory approximations. Here we show the water dissociation
free energies on seven pristine TiO2 surfaces, and predict that anatase (100),
anatase (110), rutile (001), and rutile (011) favor water dissociation, anatase
(101) and rutile (100) have mostly molecular adsorption, while the simulations
of rutile (110) sensitively depend on the slab thickness and molecular adsorption
is preferred with thick slabs. Moreover, using an automated algorithm, we reveal
that these surfaces follow different types of atomistic mechanisms for proton
transfer and water dissociation: one-step, two-step, or both. These mechanisms
can be rationalized based on the arrangements of water molecules on the different
surfaces. Our finding thus demonstrates that the different pristine TiO2 surfaces
react with water in distinct ways, and cannot be represented using just the low-energy
anatase (101) and rutile (110) surfaces.'
acknowledgement: F.S., J.H., and B.C. thank the Swiss National Supercomputing Centre
(CSCS) for the generous allocation of CPU hours via production project s1108 at
the Piz Daint supercomputer. B.C. acknowledges resources provided by the Cambridge
Tier-2 system operated by the University of Cambridge Research Computing Service
funded by EPSRC Tier-2 capital grant EP/P020259/1. J.C. acknowledges the Beijing
Natural Science Foundation for support under grant No. JQ22001. F.S., and J.H. thank
the Swiss Platform for Advanced Scientific Computing (PASC) via the 2021-2024 “Ab
Initio Molecular Dynamics at the Exa-Scale” project. This project has received funding
from the European Union’s Horizon 2020 research and innovation programme under the
Marie Skłodowska-Curie grant agreement No 101034413.
article_number: '6131'
article_processing_charge: Yes
article_type: original
arxiv: 1
author:
- first_name: Zezhu
full_name: Zeng, Zezhu
id: 54a2c730-803f-11ed-ab7e-95b29d2680e7
last_name: Zeng
- first_name: Felix
full_name: Wodaczek, Felix
id: 8b4b6a9f-32b0-11ee-9fa8-bbe85e26258e
last_name: Wodaczek
orcid: 0009-0000-1457-795X
- first_name: Keyang
full_name: Liu, Keyang
last_name: Liu
- first_name: Frederick
full_name: Stein, Frederick
last_name: Stein
- first_name: Jürg
full_name: Hutter, Jürg
last_name: Hutter
- first_name: Ji
full_name: Chen, Ji
last_name: Chen
- first_name: Bingqing
full_name: Cheng, Bingqing
id: cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9
last_name: Cheng
orcid: 0000-0002-3584-9632
citation:
ama: Zeng Z, Wodaczek F, Liu K, et al. Mechanistic insight on water dissociation
on pristine low-index TiO2 surfaces from machine learning molecular dynamics simulations.
Nature Communications. 2023;14. doi:10.1038/s41467-023-41865-8
apa: Zeng, Z., Wodaczek, F., Liu, K., Stein, F., Hutter, J., Chen, J., & Cheng,
B. (2023). Mechanistic insight on water dissociation on pristine low-index TiO2
surfaces from machine learning molecular dynamics simulations. Nature Communications.
Springer Nature. https://doi.org/10.1038/s41467-023-41865-8
chicago: Zeng, Zezhu, Felix Wodaczek, Keyang Liu, Frederick Stein, Jürg Hutter,
Ji Chen, and Bingqing Cheng. “Mechanistic Insight on Water Dissociation on Pristine
Low-Index TiO2 Surfaces from Machine Learning Molecular Dynamics Simulations.”
Nature Communications. Springer Nature, 2023. https://doi.org/10.1038/s41467-023-41865-8.
ieee: Z. Zeng et al., “Mechanistic insight on water dissociation on pristine
low-index TiO2 surfaces from machine learning molecular dynamics simulations,”
Nature Communications, vol. 14. Springer Nature, 2023.
ista: Zeng Z, Wodaczek F, Liu K, Stein F, Hutter J, Chen J, Cheng B. 2023. Mechanistic
insight on water dissociation on pristine low-index TiO2 surfaces from machine
learning molecular dynamics simulations. Nature Communications. 14, 6131.
mla: Zeng, Zezhu, et al. “Mechanistic Insight on Water Dissociation on Pristine
Low-Index TiO2 Surfaces from Machine Learning Molecular Dynamics Simulations.”
Nature Communications, vol. 14, 6131, Springer Nature, 2023, doi:10.1038/s41467-023-41865-8.
short: Z. Zeng, F. Wodaczek, K. Liu, F. Stein, J. Hutter, J. Chen, B. Cheng, Nature
Communications 14 (2023).
date_created: 2023-10-15T22:01:10Z
date_published: 2023-10-02T00:00:00Z
date_updated: 2023-12-13T13:02:07Z
day: '02'
ddc:
- '540'
- '000'
department:
- _id: BiCh
- _id: GradSch
doi: 10.1038/s41467-023-41865-8
ec_funded: 1
external_id:
arxiv:
- '2303.07433'
isi:
- '001084354900008'
pmid:
- '37783698'
file:
- access_level: open_access
checksum: 7d1dffd36b672ec679f08f70ce79da87
content_type: application/pdf
creator: dernst
date_created: 2023-10-16T07:34:49Z
date_updated: 2023-10-16T07:34:49Z
file_id: '14432'
file_name: 2023_NatureComm_Zeng.pdf
file_size: 3194116
relation: main_file
success: 1
file_date_updated: 2023-10-16T07:34:49Z
has_accepted_license: '1'
intvolume: ' 14'
isi: 1
language:
- iso: eng
license: https://creativecommons.org/licenses/by/4.0/
month: '10'
oa: 1
oa_version: Published Version
pmid: 1
project:
- _id: fc2ed2f7-9c52-11eb-aca3-c01059dda49c
call_identifier: H2020
grant_number: '101034413'
name: 'IST-BRIDGE: International postdoctoral program'
publication: Nature Communications
publication_identifier:
eissn:
- 2041-1723
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://github.com/BingqingCheng/TiO2-water
scopus_import: '1'
status: public
title: Mechanistic insight on water dissociation on pristine low-index TiO2 surfaces
from machine learning molecular dynamics simulations
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 14
year: '2023'
...
---
_id: '14603'
abstract:
- lang: eng
text: Computing the solubility of crystals in a solvent using atomistic simulations
is notoriously challenging due to the complexities and convergence issues associated
with free-energy methods, as well as the slow equilibration in direct-coexistence
simulations. This paper introduces a molecular-dynamics workflow that simplifies
and robustly computes the solubility of molecular or ionic crystals. This method
is considerably more straightforward than the state-of-the-art, as we have streamlined
and optimised each step of the process. Specifically, we calculate the chemical
potential of the crystal using the gas-phase molecule as a reference state, and
employ the S0 method to determine the concentration dependence of the chemical
potential of the solute. We use this workflow to predict the solubilities of sodium
chloride in water, urea polymorphs in water, and paracetamol polymorphs in both
water and ethanol. Our findings indicate that the predicted solubility is sensitive
to the chosen potential energy surface. Furthermore, we note that the harmonic
approximation often fails for both molecular crystals and gas molecules at or
above room temperature, and that the assumption of an ideal solution becomes less
valid for highly soluble substances.
acknowledgement: A.R. and B.C. acknowledge resources provided by the Cambridge Tier-2
system operated by the University of Cambridge Research Computing Service funded
by EPSRC Tier-2 capital Grant No. EP/P020259/1. P.Y.C. acknowledges support from
the Ernest Oppenheimer Fund and the Winton Programme for the Physics of Sustainability.
article_number: '184110'
article_processing_charge: Yes (in subscription journal)
article_type: original
arxiv: 1
author:
- first_name: Aleks
full_name: Reinhardt, Aleks
last_name: Reinhardt
- first_name: Pin Yu
full_name: Chew, Pin Yu
last_name: Chew
- first_name: Bingqing
full_name: Cheng, Bingqing
id: cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9
last_name: Cheng
orcid: 0000-0002-3584-9632
citation:
ama: Reinhardt A, Chew PY, Cheng B. A streamlined molecular-dynamics workflow for
computing solubilities of molecular and ionic crystals. Journal of Chemical
Physics. 2023;159(18). doi:10.1063/5.0173341
apa: Reinhardt, A., Chew, P. Y., & Cheng, B. (2023). A streamlined molecular-dynamics
workflow for computing solubilities of molecular and ionic crystals. Journal
of Chemical Physics. AIP Publishing. https://doi.org/10.1063/5.0173341
chicago: Reinhardt, Aleks, Pin Yu Chew, and Bingqing Cheng. “A Streamlined Molecular-Dynamics
Workflow for Computing Solubilities of Molecular and Ionic Crystals.” Journal
of Chemical Physics. AIP Publishing, 2023. https://doi.org/10.1063/5.0173341.
ieee: A. Reinhardt, P. Y. Chew, and B. Cheng, “A streamlined molecular-dynamics
workflow for computing solubilities of molecular and ionic crystals,” Journal
of Chemical Physics, vol. 159, no. 18. AIP Publishing, 2023.
ista: Reinhardt A, Chew PY, Cheng B. 2023. A streamlined molecular-dynamics workflow
for computing solubilities of molecular and ionic crystals. Journal of Chemical
Physics. 159(18), 184110.
mla: Reinhardt, Aleks, et al. “A Streamlined Molecular-Dynamics Workflow for Computing
Solubilities of Molecular and Ionic Crystals.” Journal of Chemical Physics,
vol. 159, no. 18, 184110, AIP Publishing, 2023, doi:10.1063/5.0173341.
short: A. Reinhardt, P.Y. Chew, B. Cheng, Journal of Chemical Physics 159 (2023).
date_created: 2023-11-26T23:00:54Z
date_published: 2023-11-14T00:00:00Z
date_updated: 2023-11-28T08:39:23Z
day: '14'
ddc:
- '530'
- '540'
department:
- _id: BiCh
doi: 10.1063/5.0173341
external_id:
arxiv:
- '2308.10886'
file:
- access_level: open_access
checksum: f668ee0d07096eef81159d05bc27aabc
content_type: application/pdf
creator: dernst
date_created: 2023-11-28T08:39:06Z
date_updated: 2023-11-28T08:39:06Z
file_id: '14620'
file_name: 2023_JourChemicalPhysics_Reinhardt.pdf
file_size: 6276059
relation: main_file
success: 1
file_date_updated: 2023-11-28T08:39:06Z
has_accepted_license: '1'
intvolume: ' 159'
issue: '18'
language:
- iso: eng
month: '11'
oa: 1
oa_version: Published Version
publication: Journal of Chemical Physics
publication_identifier:
eissn:
- 1089-7690
issn:
- 0021-9606
publication_status: published
publisher: AIP Publishing
quality_controlled: '1'
related_material:
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relation: research_data
status: public
scopus_import: '1'
status: public
title: A streamlined molecular-dynamics workflow for computing solubilities of molecular
and ionic crystals
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 159
year: '2023'
...
---
_id: '14619'
abstract:
- lang: eng
text: Data underlying the publication "A streamlined molecular-dynamics workflow
for computing solubilities of molecular and ionic crystals" (DOI https://doi.org/10.1063/5.0173341).
article_processing_charge: No
author:
- first_name: Bingqing
full_name: Cheng, Bingqing
id: cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9
last_name: Cheng
orcid: 0000-0002-3584-9632
citation:
ama: 'Cheng B. BingqingCheng/solubility: V1.0. 2023. doi:10.5281/ZENODO.8398094'
apa: 'Cheng, B. (2023). BingqingCheng/solubility: V1.0. Zenodo. https://doi.org/10.5281/ZENODO.8398094'
chicago: 'Cheng, Bingqing. “BingqingCheng/Solubility: V1.0.” Zenodo, 2023. https://doi.org/10.5281/ZENODO.8398094.'
ieee: 'B. Cheng, “BingqingCheng/solubility: V1.0.” Zenodo, 2023.'
ista: 'Cheng B. 2023. BingqingCheng/solubility: V1.0, Zenodo, 10.5281/ZENODO.8398094.'
mla: 'Cheng, Bingqing. BingqingCheng/Solubility: V1.0. Zenodo, 2023, doi:10.5281/ZENODO.8398094.'
short: B. Cheng, (2023).
date_created: 2023-11-28T08:32:18Z
date_published: 2023-10-02T00:00:00Z
date_updated: 2023-11-28T08:39:22Z
day: '02'
ddc:
- '530'
department:
- _id: BiCh
doi: 10.5281/ZENODO.8398094
has_accepted_license: '1'
main_file_link:
- open_access: '1'
url: https://doi.org/10.5281/zenodo.8398094
month: '10'
oa: 1
oa_version: Published Version
publisher: Zenodo
related_material:
record:
- id: '14603'
relation: used_in_publication
status: public
status: public
title: 'BingqingCheng/solubility: V1.0'
type: research_data_reference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...
---
_id: '13216'
abstract:
- lang: eng
text: Physical catalysts often have multiple sites where reactions can take place.
One prominent example is single-atom alloys, where the reactive dopant atoms can
preferentially locate in the bulk or at different sites on the surface of the
nanoparticle. However, ab initio modeling of catalysts usually only considers
one site of the catalyst, neglecting the effects of multiple sites. Here, nanoparticles
of copper doped with single-atom rhodium or palladium are modeled for the dehydrogenation
of propane. Single-atom alloy nanoparticles are simulated at 400–600 K, using
machine learning potentials trained on density functional theory calculations,
and then the occupation of different single-atom active sites is identified using
a similarity kernel. Further, the turnover frequency for all possible sites is
calculated for propane dehydrogenation to propene through microkinetic modeling
using density functional theory calculations. The total turnover frequencies of
the whole nanoparticle are then described from both the population and the individual
turnover frequency of each site. Under operating conditions, rhodium as a dopant
is found to almost exclusively occupy (111) surface sites while palladium as a
dopant occupies a greater variety of facets. Undercoordinated dopant surface sites
are found to tend to be more reactive for propane dehydrogenation compared to
the (111) surface. It is found that considering the dynamics of the single-atom
alloy nanoparticle has a profound effect on the calculated catalytic activity
of single-atom alloys by several orders of magnitude.
acknowledgement: "B.C. acknowledges resources provided by the Cambridge Tier2 system
operated by the University of Cambridge Research\r\nComputing Service funded by
EPSRC Tier-2 capital grant EP/\r\nP020259/1."
article_processing_charge: Yes (via OA deal)
article_type: original
author:
- first_name: Rhys
full_name: Bunting, Rhys
id: 91deeae8-1207-11ec-b130-c194ad5b50c6
last_name: Bunting
orcid: 0000-0001-6928-074X
- first_name: Felix
full_name: Wodaczek, Felix
id: 8b4b6a9f-32b0-11ee-9fa8-bbe85e26258e
last_name: Wodaczek
orcid: 0009-0000-1457-795X
- first_name: Tina
full_name: Torabi, Tina
last_name: Torabi
- first_name: Bingqing
full_name: Cheng, Bingqing
id: cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9
last_name: Cheng
orcid: 0000-0002-3584-9632
citation:
ama: 'Bunting R, Wodaczek F, Torabi T, Cheng B. Reactivity of single-atom alloy
nanoparticles: Modeling the dehydrogenation of propane. Journal of the American
Chemical Society. 2023;145(27):14894-14902. doi:10.1021/jacs.3c04030'
apa: 'Bunting, R., Wodaczek, F., Torabi, T., & Cheng, B. (2023). Reactivity
of single-atom alloy nanoparticles: Modeling the dehydrogenation of propane. Journal
of the American Chemical Society. American Chemical Society. https://doi.org/10.1021/jacs.3c04030'
chicago: 'Bunting, Rhys, Felix Wodaczek, Tina Torabi, and Bingqing Cheng. “Reactivity
of Single-Atom Alloy Nanoparticles: Modeling the Dehydrogenation of Propane.”
Journal of the American Chemical Society. American Chemical Society, 2023.
https://doi.org/10.1021/jacs.3c04030.'
ieee: 'R. Bunting, F. Wodaczek, T. Torabi, and B. Cheng, “Reactivity of single-atom
alloy nanoparticles: Modeling the dehydrogenation of propane,” Journal of the
American Chemical Society, vol. 145, no. 27. American Chemical Society, pp.
14894–14902, 2023.'
ista: 'Bunting R, Wodaczek F, Torabi T, Cheng B. 2023. Reactivity of single-atom
alloy nanoparticles: Modeling the dehydrogenation of propane. Journal of the American
Chemical Society. 145(27), 14894–14902.'
mla: 'Bunting, Rhys, et al. “Reactivity of Single-Atom Alloy Nanoparticles: Modeling
the Dehydrogenation of Propane.” Journal of the American Chemical Society,
vol. 145, no. 27, American Chemical Society, 2023, pp. 14894–902, doi:10.1021/jacs.3c04030.'
short: R. Bunting, F. Wodaczek, T. Torabi, B. Cheng, Journal of the American Chemical
Society 145 (2023) 14894–14902.
date_created: 2023-07-12T09:16:40Z
date_published: 2023-06-30T00:00:00Z
date_updated: 2023-10-11T08:45:10Z
day: '30'
ddc:
- '540'
department:
- _id: MaIb
- _id: BiCh
doi: 10.1021/jacs.3c04030
external_id:
isi:
- '001020623900001'
pmid:
- '37390457'
file:
- access_level: open_access
checksum: e07d5323f9c0e5cbd1ad6453f29440ab
content_type: application/pdf
creator: cchlebak
date_created: 2023-07-12T10:22:04Z
date_updated: 2023-07-12T10:22:04Z
file_id: '13219'
file_name: 2023_JACS_Bunting.pdf
file_size: 3155843
relation: main_file
success: 1
file_date_updated: 2023-07-12T10:22:04Z
has_accepted_license: '1'
intvolume: ' 145'
isi: 1
issue: '27'
keyword:
- Colloid and Surface Chemistry
- Biochemistry
- General Chemistry
- Catalysis
language:
- iso: eng
month: '06'
oa: 1
oa_version: Published Version
page: 14894-14902
pmid: 1
publication: Journal of the American Chemical Society
publication_identifier:
eissn:
- 1520-5126
issn:
- 0002-7863
publication_status: published
publisher: American Chemical Society
quality_controlled: '1'
status: public
title: 'Reactivity of single-atom alloy nanoparticles: Modeling the dehydrogenation
of propane'
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9
volume: 145
year: '2023'
...
---
_id: '12702'
abstract:
- lang: eng
text: Hydrocarbon mixtures are extremely abundant in the Universe, and diamond formation
from them can play a crucial role in shaping the interior structure and evolution
of planets. With first-principles accuracy, we first estimate the melting line
of diamond, and then reveal the nature of chemical bonding in hydrocarbons at
extreme conditions. We finally establish the pressure-temperature phase boundary
where it is thermodynamically possible for diamond to form from hydrocarbon mixtures
with different atomic fractions of carbon. Notably, here we show a depletion zone
at pressures above 200 GPa and temperatures below 3000 K-3500 K where diamond
formation is thermodynamically favorable regardless of the carbon atomic fraction,
due to a phase separation mechanism. The cooler condition of the interior of Neptune
compared to Uranus means that the former is much more likely to contain the depletion
zone. Our findings can help explain the dichotomy of the two ice giants manifested
by the low luminosity of Uranus, and lead to a better understanding of (exo-)planetary
formation and evolution.
acknowledgement: BC thanks Daan Frenkel for stimulating discussions. We thank Aleks
Reinhardt, Daan Frenkel, Marius Millot, Federica Coppari, Rhys Bunting, and Chris
J. Pickard for critically reading the manuscript and providing useful suggestions.
BC acknowledges resources provided by the Cambridge Tier-2 system operated by the
University of Cambridge Research Computing Service funded by EPSRC Tier-2 capital
grant EP/P020259/1. SH acknowledges support from LDRD 19-ERD-031 and computing support
from the Lawrence Livermore National Laboratory (LLNL) Institutional Computing Grand
Challenge program. Lawrence Livermore National Laboratory is operated by Lawrence
Livermore National Security, LLC, for the U.S. Department of Energy, National Nuclear
Security Administration under Contract DE-AC52-07NA27344. MB acknowledges support
by the European Horizon 2020 program within the Marie Skłodowska-Curie actions (xICE
grant number 894725), funding from the NOMIS foundation and computational resources
at the North-German Supercomputing Alliance (HLRN) facilities.
article_number: '1104'
article_processing_charge: No
article_type: original
author:
- first_name: Bingqing
full_name: Cheng, Bingqing
id: cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9
last_name: Cheng
orcid: 0000-0002-3584-9632
- first_name: Sebastien
full_name: Hamel, Sebastien
last_name: Hamel
- first_name: Mandy
full_name: Bethkenhagen, Mandy
id: 201939f4-803f-11ed-ab7e-d8da4bd1517f
last_name: Bethkenhagen
orcid: 0000-0002-1838-2129
citation:
ama: Cheng B, Hamel S, Bethkenhagen M. Thermodynamics of diamond formation from
hydrocarbon mixtures in planets. Nature Communications. 2023;14. doi:10.1038/s41467-023-36841-1
apa: Cheng, B., Hamel, S., & Bethkenhagen, M. (2023). Thermodynamics of diamond
formation from hydrocarbon mixtures in planets. Nature Communications.
Springer Nature. https://doi.org/10.1038/s41467-023-36841-1
chicago: Cheng, Bingqing, Sebastien Hamel, and Mandy Bethkenhagen. “Thermodynamics
of Diamond Formation from Hydrocarbon Mixtures in Planets.” Nature Communications.
Springer Nature, 2023. https://doi.org/10.1038/s41467-023-36841-1.
ieee: B. Cheng, S. Hamel, and M. Bethkenhagen, “Thermodynamics of diamond formation
from hydrocarbon mixtures in planets,” Nature Communications, vol. 14.
Springer Nature, 2023.
ista: Cheng B, Hamel S, Bethkenhagen M. 2023. Thermodynamics of diamond formation
from hydrocarbon mixtures in planets. Nature Communications. 14, 1104.
mla: Cheng, Bingqing, et al. “Thermodynamics of Diamond Formation from Hydrocarbon
Mixtures in Planets.” Nature Communications, vol. 14, 1104, Springer Nature,
2023, doi:10.1038/s41467-023-36841-1.
short: B. Cheng, S. Hamel, M. Bethkenhagen, Nature Communications 14 (2023).
date_created: 2023-03-05T23:01:04Z
date_published: 2023-02-27T00:00:00Z
date_updated: 2023-08-01T13:36:11Z
day: '27'
ddc:
- '540'
department:
- _id: BiCh
doi: 10.1038/s41467-023-36841-1
external_id:
isi:
- '000939678300002'
pmid:
- '36843123'
file:
- access_level: open_access
checksum: 5ff61ad21511950c15abb73b18613883
content_type: application/pdf
creator: cchlebak
date_created: 2023-03-07T10:58:00Z
date_updated: 2023-03-07T10:58:00Z
file_id: '12713'
file_name: 2023_NatComm_Cheng.pdf
file_size: 1946443
relation: main_file
success: 1
file_date_updated: 2023-03-07T10:58:00Z
has_accepted_license: '1'
intvolume: ' 14'
isi: 1
language:
- iso: eng
month: '02'
oa: 1
oa_version: Published Version
pmid: 1
project:
- _id: 9B861AAC-BA93-11EA-9121-9846C619BF3A
name: NOMIS Fellowship Program
publication: Nature Communications
publication_identifier:
eissn:
- 2041-1723
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Thermodynamics of diamond formation from hydrocarbon mixtures in planets
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 14
year: '2023'
...
---
_id: '12879'
abstract:
- lang: eng
text: Machine learning (ML) has been widely applied to chemical property prediction,
most prominently for the energies and forces in molecules and materials. The strong
interest in predicting energies in particular has led to a ‘local energy’-based
paradigm for modern atomistic ML models, which ensures size-extensivity and a
linear scaling of computational cost with system size. However, many electronic
properties (such as excitation energies or ionization energies) do not necessarily
scale linearly with system size and may even be spatially localized. Using size-extensive
models in these cases can lead to large errors. In this work, we explore different
strategies for learning intensive and localized properties, using HOMO energies
in organic molecules as a representative test case. In particular, we analyze
the pooling functions that atomistic neural networks use to predict molecular
properties, and suggest an orbital weighted average (OWA) approach that enables
the accurate prediction of orbital energies and locations.
acknowledgement: KC acknowledges funding from the China Scholarship Council. KC is
grateful for the TUM graduate school finance support to visit Bingqing Cheng's group
in IST for two months. We also thankfully acknowledge computational resources provided
by the MPCDF Supercomputing Centre.
article_processing_charge: No
article_type: original
author:
- first_name: Ke
full_name: Chen, Ke
id: c636c5ca-e8b8-11ed-b2d4-cc2c37613a8d
last_name: Chen
- first_name: Christian
full_name: Kunkel, Christian
last_name: Kunkel
- first_name: Bingqing
full_name: Cheng, Bingqing
id: cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9
last_name: Cheng
orcid: 0000-0002-3584-9632
- first_name: Karsten
full_name: Reuter, Karsten
last_name: Reuter
- first_name: Johannes T.
full_name: Margraf, Johannes T.
last_name: Margraf
citation:
ama: Chen K, Kunkel C, Cheng B, Reuter K, Margraf JT. Physics-inspired machine learning
of localized intensive properties. Chemical Science. 2023. doi:10.1039/d3sc00841j
apa: Chen, K., Kunkel, C., Cheng, B., Reuter, K., & Margraf, J. T. (2023). Physics-inspired
machine learning of localized intensive properties. Chemical Science. Royal
Society of Chemistry. https://doi.org/10.1039/d3sc00841j
chicago: Chen, Ke, Christian Kunkel, Bingqing Cheng, Karsten Reuter, and Johannes
T. Margraf. “Physics-Inspired Machine Learning of Localized Intensive Properties.”
Chemical Science. Royal Society of Chemistry, 2023. https://doi.org/10.1039/d3sc00841j.
ieee: K. Chen, C. Kunkel, B. Cheng, K. Reuter, and J. T. Margraf, “Physics-inspired
machine learning of localized intensive properties,” Chemical Science.
Royal Society of Chemistry, 2023.
ista: Chen K, Kunkel C, Cheng B, Reuter K, Margraf JT. 2023. Physics-inspired machine
learning of localized intensive properties. Chemical Science.
mla: Chen, Ke, et al. “Physics-Inspired Machine Learning of Localized Intensive
Properties.” Chemical Science, Royal Society of Chemistry, 2023, doi:10.1039/d3sc00841j.
short: K. Chen, C. Kunkel, B. Cheng, K. Reuter, J.T. Margraf, Chemical Science (2023).
date_created: 2023-04-30T22:01:06Z
date_published: 2023-04-10T00:00:00Z
date_updated: 2023-08-01T14:18:10Z
day: '10'
ddc:
- '000'
- '540'
department:
- _id: BiCh
doi: 10.1039/d3sc00841j
external_id:
isi:
- '000971508100001'
file:
- access_level: open_access
checksum: 5eeec69a51e192dcd94b955d84423836
content_type: application/pdf
creator: dernst
date_created: 2023-05-02T07:17:05Z
date_updated: 2023-05-02T07:17:05Z
file_id: '12883'
file_name: 2023_ChemialScience_Chen.pdf
file_size: 1515446
relation: main_file
success: 1
file_date_updated: 2023-05-02T07:17:05Z
has_accepted_license: '1'
isi: 1
language:
- iso: eng
license: https://creativecommons.org/licenses/by/3.0/
month: '04'
oa: 1
oa_version: Published Version
publication: Chemical Science
publication_identifier:
eissn:
- 2041-6539
issn:
- 2041-6520
publication_status: published
publisher: Royal Society of Chemistry
quality_controlled: '1'
scopus_import: '1'
status: public
title: Physics-inspired machine learning of localized intensive properties
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/3.0/legalcode
name: Creative Commons Attribution 3.0 Unported (CC BY 3.0)
short: CC BY (3.0)
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
year: '2023'
...
---
_id: '12912'
abstract:
- lang: eng
text: The chemical potential of adsorbed or confined fluids provides insight into
their unique thermodynamic properties and determines adsorption isotherms. However,
it is often difficult to compute this quantity from atomistic simulations using
existing statistical mechanical methods. We introduce a computational framework
that utilizes static structure factors, thermodynamic integration, and free energy
perturbation for calculating the absolute chemical potential of fluids. For demonstration,
we apply the method to compute the adsorption isotherms of carbon dioxide in a
metal-organic framework and water in carbon nanotubes.
acknowledgement: We thank Aleks Reinhardt and Daan Frenkel for their insightful comments
and suggestions on the article. B.C. acknowledges the resources provided by the
Cambridge Tier-2 system operated by the University of Cambridge Research Computing
Service funded by EPSRC Tier-2 capital Grant No. EP/P020259/1.
article_number: '161101 '
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Rochus
full_name: Schmid, Rochus
last_name: Schmid
- first_name: Bingqing
full_name: Cheng, Bingqing
id: cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9
last_name: Cheng
orcid: 0000-0002-3584-9632
citation:
ama: Schmid R, Cheng B. Computing chemical potentials of adsorbed or confined fluids.
The Journal of Chemical Physics. 2023;158(16). doi:10.1063/5.0146711
apa: Schmid, R., & Cheng, B. (2023). Computing chemical potentials of adsorbed
or confined fluids. The Journal of Chemical Physics. AIP Publishing. https://doi.org/10.1063/5.0146711
chicago: Schmid, Rochus, and Bingqing Cheng. “Computing Chemical Potentials of Adsorbed
or Confined Fluids.” The Journal of Chemical Physics. AIP Publishing, 2023.
https://doi.org/10.1063/5.0146711.
ieee: R. Schmid and B. Cheng, “Computing chemical potentials of adsorbed or confined
fluids,” The Journal of Chemical Physics, vol. 158, no. 16. AIP Publishing,
2023.
ista: Schmid R, Cheng B. 2023. Computing chemical potentials of adsorbed or confined
fluids. The Journal of Chemical Physics. 158(16), 161101.
mla: Schmid, Rochus, and Bingqing Cheng. “Computing Chemical Potentials of Adsorbed
or Confined Fluids.” The Journal of Chemical Physics, vol. 158, no. 16,
161101, AIP Publishing, 2023, doi:10.1063/5.0146711.
short: R. Schmid, B. Cheng, The Journal of Chemical Physics 158 (2023).
date_created: 2023-05-07T22:01:03Z
date_published: 2023-04-24T00:00:00Z
date_updated: 2023-08-01T14:34:49Z
day: '24'
ddc:
- '540'
department:
- _id: BiCh
doi: 10.1063/5.0146711
external_id:
arxiv:
- '2302.01297'
isi:
- '001010676000010'
pmid:
- '37093149'
file:
- access_level: open_access
checksum: 4ab8c965f2fa4e17920bfa846847f137
content_type: application/pdf
creator: dernst
date_created: 2023-05-08T07:44:49Z
date_updated: 2023-05-08T07:44:49Z
file_id: '12918'
file_name: 2023_JourChemicalPhysics_Schmid.pdf
file_size: 6499468
relation: main_file
success: 1
file_date_updated: 2023-05-08T07:44:49Z
has_accepted_license: '1'
intvolume: ' 158'
isi: 1
issue: '16'
language:
- iso: eng
month: '04'
oa: 1
oa_version: Published Version
pmid: 1
publication: The Journal of Chemical Physics
publication_identifier:
eissn:
- 1089-7690
publication_status: published
publisher: AIP Publishing
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://github.com/BingqingCheng/mu-adsorption
- relation: software
url: https://github.com/BingqingCheng/S0
scopus_import: '1'
status: public
title: Computing chemical potentials of adsorbed or confined fluids
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 158
year: '2023'
...
---
_id: '10827'
abstract:
- lang: eng
text: Titanium dioxide has been extensively studied in the rutile or anatase phase,
while its high-pressure phases are less well-understood, despite that many are
thought to have interesting optical, mechanical, and electrochemical properties.
First-principles methods, such as density functional theory (DFT), are often used
to compute the enthalpies of TiO2 phases at 0 K, but they are expensive and, thus,
impractical for long time scale and large system-size simulations at finite temperatures.
On the other hand, cheap empirical potentials fail to capture the relative stabilities
of various polymorphs. To model the thermodynamic behaviors of ambient and high-pressure
phases of TiO2, we design an empirical model as a baseline and then train a machine
learning potential based on the difference between the DFT data and the empirical
model. This so-called Δ-learning potential contains long-range electrostatic interactions
and predicts the 0 K enthalpies of stable TiO2 phases that are in good agreement
with DFT. We construct a pressure–temperature phase diagram of TiO2 in the range
0 < P < 70 GPa and 100 < T < 1500 K. We then simulate dynamic phase transition
processes by compressing anatase at different temperatures. At 300 K, we predominantly
observe an anatase-to-baddeleyite transformation at about 20 GPa via a martensitic
two-step mechanism with a highly ordered and collective atomic motion. At 2000
K, anatase can transform into cotunnite around 45–55 GPa in a thermally activated
and probabilistic manner, accompanied by diffusive movement of oxygen atoms. The
pressures computed for these transitions show good agreement with experiments.
Our results shed light on how to synthesize and stabilize high-pressure TiO2 phases,
and our method is generally applicable to other functional materials with multiple
polymorphs.
acknowledgement: J.G.L. and B.C. acknowledge the resources provided by the Cambridge
Tier-2 system operated by the University of Cambridge Research Computing Service
funded by the EPSRC Tier-2 capital (Grant No. EP/P020259/1).
article_number: '074106'
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Jacob G.
full_name: Lee, Jacob G.
last_name: Lee
- first_name: Chris J.
full_name: Pickard, Chris J.
last_name: Pickard
- first_name: Bingqing
full_name: Cheng, Bingqing
id: cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9
last_name: Cheng
orcid: 0000-0002-3584-9632
citation:
ama: Lee JG, Pickard CJ, Cheng B. High-pressure phase behaviors of titanium dioxide
revealed by a Δ-learning potential. The Journal of chemical physics. 2022;156(7).
doi:10.1063/5.0079844
apa: Lee, J. G., Pickard, C. J., & Cheng, B. (2022). High-pressure phase behaviors
of titanium dioxide revealed by a Δ-learning potential. The Journal of Chemical
Physics. AIP Publishing. https://doi.org/10.1063/5.0079844
chicago: Lee, Jacob G., Chris J. Pickard, and Bingqing Cheng. “High-Pressure Phase
Behaviors of Titanium Dioxide Revealed by a Δ-Learning Potential.” The Journal
of Chemical Physics. AIP Publishing, 2022. https://doi.org/10.1063/5.0079844.
ieee: J. G. Lee, C. J. Pickard, and B. Cheng, “High-pressure phase behaviors of
titanium dioxide revealed by a Δ-learning potential,” The Journal of chemical
physics, vol. 156, no. 7. AIP Publishing, 2022.
ista: Lee JG, Pickard CJ, Cheng B. 2022. High-pressure phase behaviors of titanium
dioxide revealed by a Δ-learning potential. The Journal of chemical physics. 156(7),
074106.
mla: Lee, Jacob G., et al. “High-Pressure Phase Behaviors of Titanium Dioxide Revealed
by a Δ-Learning Potential.” The Journal of Chemical Physics, vol. 156,
no. 7, 074106, AIP Publishing, 2022, doi:10.1063/5.0079844.
short: J.G. Lee, C.J. Pickard, B. Cheng, The Journal of Chemical Physics 156 (2022).
date_created: 2022-03-06T23:01:53Z
date_published: 2022-02-16T00:00:00Z
date_updated: 2023-08-02T14:45:46Z
day: '16'
department:
- _id: BiCh
doi: 10.1063/5.0079844
external_id:
arxiv:
- '2111.12968'
isi:
- '000796704500014'
intvolume: ' 156'
isi: 1
issue: '7'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2111.12968
month: '02'
oa: 1
oa_version: Preprint
publication: The Journal of chemical physics
publication_identifier:
eissn:
- '10897690'
publication_status: published
publisher: AIP Publishing
quality_controlled: '1'
scopus_import: '1'
status: public
title: High-pressure phase behaviors of titanium dioxide revealed by a Δ-learning
potential
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 156
year: '2022'
...
---
_id: '11937'
abstract:
- lang: eng
text: Most experimentally known high-pressure ice phases have a body-centred cubic
(bcc) oxygen lattice. Our large-scale molecular-dynamics simulations with a machine-learning
potential indicate that, amongst these bcc ice phases, ices VII, VII′ and X are
the same thermodynamic phase under different conditions, whereas superionic ice
VII″ has a first-order phase boundary with ice VII′. Moreover, at about 300 GPa,
the transformation between ice X and the Pbcm phase has a sharp structural change
but no apparent activation barrier, whilst at higher pressures the barrier gradually
increases. Our study thus clarifies the phase behaviour of the high-pressure ices
and reveals peculiar solid–solid transition mechanisms not known in other systems.
acknowledgement: We thank Chris Pickard for providing the initial structures of high-pressure
ice phases and for useful advice. A.R. and B.C. acknowledge resources provided by
the Cambridge Tier-2 system operated by the University of Cambridge Research Computing
Service funded by EPSRC Tier-2 capital grant EP/P020259/1. M.B. was supported by
the European Union within the Marie Skłodowska-Curie actions (xICE grant 894725)
and acknowledges computational resources at North-German Supercomputing Alliance
(HLRN) facilities. S.H. and M.M. acknowledge support from LDRD 19-ERD-031 and computing
support from the Lawrence Livermore National Laboratory (LLNL) Institutional Computing
Grand Challenge programme. F.C. acknowledges support from the US DOE Office of Science,
Office of Fusion Energy Sciences. Lawrence Livermore National Laboratory is operated
by Lawrence Livermore National Security, LLC, for the U.S. Department of Energy,
National Nuclear Security Administration under Contract DE-AC52-07NA27344.
article_number: '4707'
article_processing_charge: No
article_type: original
author:
- first_name: Aleks
full_name: Reinhardt, Aleks
last_name: Reinhardt
- first_name: Mandy
full_name: Bethkenhagen, Mandy
last_name: Bethkenhagen
- first_name: Federica
full_name: Coppari, Federica
last_name: Coppari
- first_name: Marius
full_name: Millot, Marius
last_name: Millot
- first_name: Sebastien
full_name: Hamel, Sebastien
last_name: Hamel
- first_name: Bingqing
full_name: Cheng, Bingqing
id: cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9
last_name: Cheng
orcid: 0000-0002-3584-9632
citation:
ama: Reinhardt A, Bethkenhagen M, Coppari F, Millot M, Hamel S, Cheng B. Thermodynamics
of high-pressure ice phases explored with atomistic simulations. Nature Communications.
2022;13. doi:10.1038/s41467-022-32374-1
apa: Reinhardt, A., Bethkenhagen, M., Coppari, F., Millot, M., Hamel, S., &
Cheng, B. (2022). Thermodynamics of high-pressure ice phases explored with atomistic
simulations. Nature Communications. Springer Nature. https://doi.org/10.1038/s41467-022-32374-1
chicago: Reinhardt, Aleks, Mandy Bethkenhagen, Federica Coppari, Marius Millot,
Sebastien Hamel, and Bingqing Cheng. “Thermodynamics of High-Pressure Ice Phases
Explored with Atomistic Simulations.” Nature Communications. Springer Nature,
2022. https://doi.org/10.1038/s41467-022-32374-1.
ieee: A. Reinhardt, M. Bethkenhagen, F. Coppari, M. Millot, S. Hamel, and B. Cheng,
“Thermodynamics of high-pressure ice phases explored with atomistic simulations,”
Nature Communications, vol. 13. Springer Nature, 2022.
ista: Reinhardt A, Bethkenhagen M, Coppari F, Millot M, Hamel S, Cheng B. 2022.
Thermodynamics of high-pressure ice phases explored with atomistic simulations.
Nature Communications. 13, 4707.
mla: Reinhardt, Aleks, et al. “Thermodynamics of High-Pressure Ice Phases Explored
with Atomistic Simulations.” Nature Communications, vol. 13, 4707, Springer
Nature, 2022, doi:10.1038/s41467-022-32374-1.
short: A. Reinhardt, M. Bethkenhagen, F. Coppari, M. Millot, S. Hamel, B. Cheng,
Nature Communications 13 (2022).
date_created: 2022-08-21T22:01:55Z
date_published: 2022-08-10T00:00:00Z
date_updated: 2023-08-03T13:00:40Z
day: '10'
ddc:
- '540'
department:
- _id: BiCh
doi: 10.1038/s41467-022-32374-1
external_id:
isi:
- '000838655300022'
pmid:
- '35948550'
file:
- access_level: open_access
checksum: 8ff9b689cde59fd3a9959a9f01929dea
content_type: application/pdf
creator: dernst
date_created: 2022-08-22T06:33:02Z
date_updated: 2022-08-22T06:33:02Z
file_id: '11939'
file_name: 2022_NatureCommunications_Reinhardt.pdf
file_size: 1767206
relation: main_file
success: 1
file_date_updated: 2022-08-22T06:33:02Z
has_accepted_license: '1'
intvolume: ' 13'
isi: 1
language:
- iso: eng
month: '08'
oa: 1
oa_version: Published Version
pmid: 1
publication: Nature Communications
publication_identifier:
eissn:
- 2041-1723
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Thermodynamics of high-pressure ice phases explored with atomistic simulations
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 13
year: '2022'
...
---
_id: '12128'
abstract:
- lang: eng
text: We introduce a machine-learning (ML) framework for high-throughput benchmarking
of diverse representations of chemical systems against datasets of materials and
molecules. The guiding principle underlying the benchmarking approach is to evaluate
raw descriptor performance by limiting model complexity to simple regression schemes
while enforcing best ML practices, allowing for unbiased hyperparameter optimization,
and assessing learning progress through learning curves along series of synchronized
train-test splits. The resulting models are intended as baselines that can inform
future method development, in addition to indicating how easily a given dataset
can be learnt. Through a comparative analysis of the training outcome across a
diverse set of physicochemical, topological and geometric representations, we
glean insight into the relative merits of these representations as well as their
interrelatedness.
acknowledgement: 'C P acknowledges funding from Astex through the Sustaining Innovation
Program under the Milner Consortium. B C acknowledges resources provided by the
Cambridge Tier-2 system operated by the University of Cambridge Research Computing
Service funded by EPSRC Tier-2 capital Grant EP/P020259/1. F A F acknowledges funding
from the Swiss National Science Foundation (Grant No. P2BSP2_191736). '
article_number: '040501'
article_processing_charge: No
article_type: original
author:
- first_name: Carl
full_name: Poelking, Carl
last_name: Poelking
- first_name: Felix A
full_name: Faber, Felix A
last_name: Faber
- first_name: Bingqing
full_name: Cheng, Bingqing
id: cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9
last_name: Cheng
orcid: 0000-0002-3584-9632
citation:
ama: 'Poelking C, Faber FA, Cheng B. BenchML: An extensible pipelining framework
for benchmarking representations of materials and molecules at scale. Machine
Learning: Science and Technology. 2022;3(4). doi:10.1088/2632-2153/ac4d11'
apa: 'Poelking, C., Faber, F. A., & Cheng, B. (2022). BenchML: An extensible
pipelining framework for benchmarking representations of materials and molecules
at scale. Machine Learning: Science and Technology. IOP Publishing. https://doi.org/10.1088/2632-2153/ac4d11'
chicago: 'Poelking, Carl, Felix A Faber, and Bingqing Cheng. “BenchML: An Extensible
Pipelining Framework for Benchmarking Representations of Materials and Molecules
at Scale.” Machine Learning: Science and Technology. IOP Publishing, 2022.
https://doi.org/10.1088/2632-2153/ac4d11.'
ieee: 'C. Poelking, F. A. Faber, and B. Cheng, “BenchML: An extensible pipelining
framework for benchmarking representations of materials and molecules at scale,”
Machine Learning: Science and Technology, vol. 3, no. 4. IOP Publishing,
2022.'
ista: 'Poelking C, Faber FA, Cheng B. 2022. BenchML: An extensible pipelining framework
for benchmarking representations of materials and molecules at scale. Machine
Learning: Science and Technology. 3(4), 040501.'
mla: 'Poelking, Carl, et al. “BenchML: An Extensible Pipelining Framework for Benchmarking
Representations of Materials and Molecules at Scale.” Machine Learning: Science
and Technology, vol. 3, no. 4, 040501, IOP Publishing, 2022, doi:10.1088/2632-2153/ac4d11.'
short: 'C. Poelking, F.A. Faber, B. Cheng, Machine Learning: Science and Technology
3 (2022).'
date_created: 2023-01-12T12:02:21Z
date_published: 2022-11-17T00:00:00Z
date_updated: 2023-08-04T08:49:53Z
day: '17'
ddc:
- '000'
department:
- _id: BiCh
doi: 10.1088/2632-2153/ac4d11
external_id:
isi:
- '000886534000001'
file:
- access_level: open_access
checksum: 8930d4ad6ed9b47358c6f1a68666adb6
content_type: application/pdf
creator: dernst
date_created: 2023-01-23T10:42:04Z
date_updated: 2023-01-23T10:42:04Z
file_id: '12343'
file_name: 2022_MachLearning_Poelking.pdf
file_size: 13814559
relation: main_file
success: 1
file_date_updated: 2023-01-23T10:42:04Z
has_accepted_license: '1'
intvolume: ' 3'
isi: 1
issue: '4'
keyword:
- Artificial Intelligence
- Human-Computer Interaction
- Software
language:
- iso: eng
month: '11'
oa: 1
oa_version: Published Version
publication: 'Machine Learning: Science and Technology'
publication_identifier:
issn:
- 2632-2153
publication_status: published
publisher: IOP Publishing
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://github.com/capoe/benchml
scopus_import: '1'
status: public
title: 'BenchML: An extensible pipelining framework for benchmarking representations
of materials and molecules at scale'
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 3
year: '2022'
...
---
_id: '12249'
abstract:
- lang: eng
text: 'The chemical potential of a component in a solution is defined as the free
energy change as the amount of that component changes. Computing this fundamental
thermodynamic property from atomistic simulations is notoriously difficult because
of the convergence issues involved in free energy methods and finite size effects.
This Communication presents the so-called S0 method, which can be used to obtain
chemical potentials from static structure factors computed from equilibrium molecular
dynamics simulations under the isothermal–isobaric ensemble. This new method is
demonstrated on the systems of binary Lennard-Jones particles, urea–water mixtures,
a NaCl aqueous solution, and a high-pressure carbon–hydrogen mixture. '
acknowledgement: I thank Daan Frenkel for providing feedback on an early draft and
for stimulating discussions, Debashish Mukherji and Robinson Cortes-Huerto for sharing
the trajectories for urea–water mixtures, and Aleks Reinhardt for useful suggestions
on the manuscript.
article_number: '121101'
article_processing_charge: No
article_type: original
author:
- first_name: Bingqing
full_name: Cheng, Bingqing
id: cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9
last_name: Cheng
orcid: 0000-0002-3584-9632
citation:
ama: Cheng B. Computing chemical potentials of solutions from structure factors.
The Journal of Chemical Physics. 2022;157(12). doi:10.1063/5.0107059
apa: Cheng, B. (2022). Computing chemical potentials of solutions from structure
factors. The Journal of Chemical Physics. AIP Publishing. https://doi.org/10.1063/5.0107059
chicago: Cheng, Bingqing. “Computing Chemical Potentials of Solutions from Structure
Factors.” The Journal of Chemical Physics. AIP Publishing, 2022. https://doi.org/10.1063/5.0107059.
ieee: B. Cheng, “Computing chemical potentials of solutions from structure factors,”
The Journal of Chemical Physics, vol. 157, no. 12. AIP Publishing, 2022.
ista: Cheng B. 2022. Computing chemical potentials of solutions from structure factors.
The Journal of Chemical Physics. 157(12), 121101.
mla: Cheng, Bingqing. “Computing Chemical Potentials of Solutions from Structure
Factors.” The Journal of Chemical Physics, vol. 157, no. 12, 121101, AIP
Publishing, 2022, doi:10.1063/5.0107059.
short: B. Cheng, The Journal of Chemical Physics 157 (2022).
date_created: 2023-01-16T09:56:20Z
date_published: 2022-09-30T00:00:00Z
date_updated: 2023-08-04T09:43:11Z
day: '30'
ddc:
- '530'
- '540'
department:
- _id: BiCh
doi: 10.1063/5.0107059
external_id:
isi:
- '000862856000003'
file:
- access_level: open_access
checksum: b0915b706568a663a9a372fca24adf35
content_type: application/pdf
creator: dernst
date_created: 2023-01-30T09:07:00Z
date_updated: 2023-01-30T09:07:00Z
file_id: '12441'
file_name: 2022_JourChemPhysics_Cheng.pdf
file_size: 4402384
relation: main_file
success: 1
file_date_updated: 2023-01-30T09:07:00Z
has_accepted_license: '1'
intvolume: ' 157'
isi: 1
issue: '12'
keyword:
- Physical and Theoretical Chemistry
- General Physics and Astronomy
language:
- iso: eng
month: '09'
oa: 1
oa_version: Published Version
publication: The Journal of Chemical Physics
publication_identifier:
eissn:
- 1089-7690
issn:
- 0021-9606
publication_status: published
publisher: AIP Publishing
quality_controlled: '1'
related_material:
link:
- relation: software
url: https://github.com/ BingqingCheng/S0
scopus_import: '1'
status: public
title: Computing chemical potentials of solutions from structure factors
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 157
year: '2022'
...
---
_id: '9669'
abstract:
- lang: eng
text: The set of known stable phases of water may not be complete, and some of the
phase boundaries between them are fuzzy. Starting from liquid water and a comprehensive
set of 50 ice structures, we compute the phase diagram at three hybrid density-functional-theory
levels of approximation, accounting for thermal and nuclear fluctuations as well
as proton disorder. Such calculations are only made tractable because we combine
machine-learning methods and advanced free-energy techniques. The computed phase
diagram is in qualitative agreement with experiment, particularly at pressures ≲ 8000
bar, and the discrepancy in chemical potential is comparable with the subtle uncertainties
introduced by proton disorder and the spread between the three hybrid functionals.
None of the hypothetical ice phases considered is thermodynamically stable in
our calculations, suggesting the completeness of the experimental water phase
diagram in the region considered. Our work demonstrates the feasibility of predicting
the phase diagram of a polymorphic system from first principles and provides a
thermodynamic way of testing the limits of quantum-mechanical calculations.
article_number: '588'
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Aleks
full_name: Reinhardt, Aleks
last_name: Reinhardt
- first_name: Bingqing
full_name: Cheng, Bingqing
id: cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9
last_name: Cheng
orcid: 0000-0002-3584-9632
citation:
ama: Reinhardt A, Cheng B. Quantum-mechanical exploration of the phase diagram of
water. Nature Communications. 2021;12(1). doi:10.1038/s41467-020-20821-w
apa: Reinhardt, A., & Cheng, B. (2021). Quantum-mechanical exploration of the
phase diagram of water. Nature Communications. Springer Nature. https://doi.org/10.1038/s41467-020-20821-w
chicago: Reinhardt, Aleks, and Bingqing Cheng. “Quantum-Mechanical Exploration of
the Phase Diagram of Water.” Nature Communications. Springer Nature, 2021.
https://doi.org/10.1038/s41467-020-20821-w.
ieee: A. Reinhardt and B. Cheng, “Quantum-mechanical exploration of the phase diagram
of water,” Nature Communications, vol. 12, no. 1. Springer Nature, 2021.
ista: Reinhardt A, Cheng B. 2021. Quantum-mechanical exploration of the phase diagram
of water. Nature Communications. 12(1), 588.
mla: Reinhardt, Aleks, and Bingqing Cheng. “Quantum-Mechanical Exploration of the
Phase Diagram of Water.” Nature Communications, vol. 12, no. 1, 588, Springer
Nature, 2021, doi:10.1038/s41467-020-20821-w.
short: A. Reinhardt, B. Cheng, Nature Communications 12 (2021).
date_created: 2021-07-15T13:48:13Z
date_published: 2021-01-26T00:00:00Z
date_updated: 2023-02-23T14:04:20Z
day: '26'
ddc:
- '530'
- '540'
doi: 10.1038/s41467-020-20821-w
extern: '1'
external_id:
arxiv:
- '2010.13729'
pmid:
- '33500405'
file:
- access_level: open_access
checksum: 8b5e1fbe2f1ab936047008043150e894
content_type: application/pdf
creator: asandaue
date_created: 2021-07-15T13:55:46Z
date_updated: 2021-07-15T13:55:46Z
file_id: '9670'
file_name: 2021_NatureCommunications_Reinhardt.pdf
file_size: 1180227
relation: main_file
success: 1
file_date_updated: 2021-07-15T13:55:46Z
has_accepted_license: '1'
intvolume: ' 12'
issue: '1'
language:
- iso: eng
month: '01'
oa: 1
oa_version: Published Version
pmid: 1
publication: Nature Communications
publication_identifier:
eissn:
- 2041-1723
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Quantum-mechanical exploration of the phase diagram of water
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf
volume: 12
year: '2021'
...
---
_id: '9695'
abstract:
- lang: eng
text: Real-world data typically contain a large number of features that are often
heterogeneous in nature, relevance, and also units of measure. When assessing
the similarity between data points, one can build various distance measures using
subsets of these features. Using the fewest features but still retaining sufficient
information about the system is crucial in many statistical learning approaches,
particularly when data are sparse. We introduce a statistical test that can assess
the relative information retained when using two different distance measures,
and determine if they are equivalent, independent, or if one is more informative
than the other. This in turn allows finding the most informative distance measure
out of a pool of candidates. The approach is applied to find the most relevant
policy variables for controlling the Covid-19 epidemic and to find compact yet
informative representations of atomic structures, but its potential applications
are wide ranging in many branches of science.
article_number: '2104.15079'
article_processing_charge: No
arxiv: 1
author:
- first_name: Aldo
full_name: Glielmo, Aldo
last_name: Glielmo
- first_name: Claudio
full_name: Zeni, Claudio
last_name: Zeni
- first_name: Bingqing
full_name: Cheng, Bingqing
id: cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9
last_name: Cheng
orcid: 0000-0002-3584-9632
- first_name: Gabor
full_name: Csanyi, Gabor
last_name: Csanyi
- first_name: Alessandro
full_name: Laio, Alessandro
last_name: Laio
citation:
ama: Glielmo A, Zeni C, Cheng B, Csanyi G, Laio A. Ranking the information content
of distance measures. arXiv.
apa: Glielmo, A., Zeni, C., Cheng, B., Csanyi, G., & Laio, A. (n.d.). Ranking
the information content of distance measures. arXiv.
chicago: Glielmo, Aldo, Claudio Zeni, Bingqing Cheng, Gabor Csanyi, and Alessandro
Laio. “Ranking the Information Content of Distance Measures.” ArXiv, n.d.
ieee: A. Glielmo, C. Zeni, B. Cheng, G. Csanyi, and A. Laio, “Ranking the information
content of distance measures,” arXiv. .
ista: Glielmo A, Zeni C, Cheng B, Csanyi G, Laio A. Ranking the information content
of distance measures. arXiv, 2104.15079.
mla: Glielmo, Aldo, et al. “Ranking the Information Content of Distance Measures.”
ArXiv, 2104.15079.
short: A. Glielmo, C. Zeni, B. Cheng, G. Csanyi, A. Laio, ArXiv (n.d.).
date_created: 2021-07-20T06:31:53Z
date_published: 2021-04-30T00:00:00Z
date_updated: 2023-02-23T14:05:13Z
day: '30'
extern: '1'
external_id:
arxiv:
- '2104.15079'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2104.15079
month: '04'
oa: 1
oa_version: Preprint
publication: arXiv
publication_status: submitted
status: public
title: Ranking the information content of distance measures
type: preprint
user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf
year: '2021'
...
---
_id: '9696'
abstract:
- lang: eng
text: Most water in the universe may be superionic, and its thermodynamic and transport
properties are crucial for planetary science but difficult to probe experimentally
or theoretically. We use machine learning and free energy methods to overcome
the limitations of quantum mechanical simulations, and characterize hydrogen diffusion,
superionic transitions, and phase behaviors of water at extreme conditions. We
predict that a close-packed superionic phase with mixed stacking is stable over
a wide temperature and pressure range, while a body-centered cubic phase is only
thermodynamically stable in a small window but is kinetically favored. Our phase
boundaries, which are consistent with the existing-albeit scarce-experimental
observations, help resolve the fractions of insulating ice, different superionic
phases, and liquid water inside of ice giants.
article_number: '2103.09035'
article_processing_charge: No
arxiv: 1
author:
- first_name: Bingqing
full_name: Cheng, Bingqing
id: cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9
last_name: Cheng
orcid: 0000-0002-3584-9632
- first_name: Mandy
full_name: Bethkenhagen, Mandy
last_name: Bethkenhagen
- first_name: Chris J.
full_name: Pickard, Chris J.
last_name: Pickard
- first_name: Sebastien
full_name: Hamel, Sebastien
last_name: Hamel
citation:
ama: Cheng B, Bethkenhagen M, Pickard CJ, Hamel S. Predicting the phase behaviors
of superionic water at planetary conditions. arXiv.
apa: Cheng, B., Bethkenhagen, M., Pickard, C. J., & Hamel, S. (n.d.). Predicting
the phase behaviors of superionic water at planetary conditions. arXiv.
chicago: Cheng, Bingqing, Mandy Bethkenhagen, Chris J. Pickard, and Sebastien Hamel.
“Predicting the Phase Behaviors of Superionic Water at Planetary Conditions.”
ArXiv, n.d.
ieee: B. Cheng, M. Bethkenhagen, C. J. Pickard, and S. Hamel, “Predicting the phase
behaviors of superionic water at planetary conditions,” arXiv. .
ista: Cheng B, Bethkenhagen M, Pickard CJ, Hamel S. Predicting the phase behaviors
of superionic water at planetary conditions. arXiv, 2103.09035.
mla: Cheng, Bingqing, et al. “Predicting the Phase Behaviors of Superionic Water
at Planetary Conditions.” ArXiv, 2103.09035.
short: B. Cheng, M. Bethkenhagen, C.J. Pickard, S. Hamel, ArXiv (n.d.).
date_created: 2021-07-20T06:42:29Z
date_published: 2021-03-16T00:00:00Z
date_updated: 2023-02-23T14:05:16Z
day: '16'
extern: '1'
external_id:
arxiv:
- '2103.09035'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2103.09035
month: '03'
oa: 1
oa_version: Preprint
publication: arXiv
publication_status: submitted
status: public
title: Predicting the phase behaviors of superionic water at planetary conditions
type: preprint
user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf
year: '2021'
...
---
_id: '9698'
abstract:
- lang: eng
text: Machine learning models are poised to make a transformative impact on chemical
sciences by dramatically accelerating computational algorithms and amplifying
insights available from computational chemistry methods. However, achieving this
requires a confluence and coaction of expertise in computer science and physical
sciences. This review is written for new and experienced researchers working at
the intersection of both fields. We first provide concise tutorials of computational
chemistry and machine learning methods, showing how insights involving both can
be achieved. We then follow with a critical review of noteworthy applications
that demonstrate how computational chemistry and machine learning can be used
together to provide insightful (and useful) predictions in molecular and materials
modeling, retrosyntheses, catalysis, and drug design.
article_processing_charge: No
article_type: review
arxiv: 1
author:
- first_name: John A.
full_name: Keith, John A.
last_name: Keith
- first_name: Valentin
full_name: Valentin Vassilev-Galindo, Valentin
last_name: Valentin Vassilev-Galindo
- first_name: Bingqing
full_name: Cheng, Bingqing
id: cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9
last_name: Cheng
orcid: 0000-0002-3584-9632
- first_name: Stefan
full_name: Chmiela, Stefan
last_name: Chmiela
- first_name: Michael
full_name: Gastegger, Michael
last_name: Gastegger
- first_name: Klaus-Robert
full_name: Müller, Klaus-Robert
last_name: Müller
- first_name: Alexandre
full_name: Tkatchenko, Alexandre
last_name: Tkatchenko
citation:
ama: Keith JA, Valentin Vassilev-Galindo V, Cheng B, et al. Combining machine learning
and computational chemistry for predictive insights into chemical systems. Chemical
Reviews. 2021;121(16):9816-9872. doi:10.1021/acs.chemrev.1c00107
apa: Keith, J. A., Valentin Vassilev-Galindo, V., Cheng, B., Chmiela, S., Gastegger,
M., Müller, K.-R., & Tkatchenko, A. (2021). Combining machine learning and
computational chemistry for predictive insights into chemical systems. Chemical
Reviews. American Chemical Society. https://doi.org/10.1021/acs.chemrev.1c00107
chicago: Keith, John A., Valentin Valentin Vassilev-Galindo, Bingqing Cheng, Stefan
Chmiela, Michael Gastegger, Klaus-Robert Müller, and Alexandre Tkatchenko. “Combining
Machine Learning and Computational Chemistry for Predictive Insights into Chemical
Systems.” Chemical Reviews. American Chemical Society, 2021. https://doi.org/10.1021/acs.chemrev.1c00107.
ieee: J. A. Keith et al., “Combining machine learning and computational chemistry
for predictive insights into chemical systems,” Chemical Reviews, vol.
121, no. 16. American Chemical Society, pp. 9816–9872, 2021.
ista: Keith JA, Valentin Vassilev-Galindo V, Cheng B, Chmiela S, Gastegger M, Müller
K-R, Tkatchenko A. 2021. Combining machine learning and computational chemistry
for predictive insights into chemical systems. Chemical Reviews. 121(16), 9816–9872.
mla: Keith, John A., et al. “Combining Machine Learning and Computational Chemistry
for Predictive Insights into Chemical Systems.” Chemical Reviews, vol.
121, no. 16, American Chemical Society, 2021, pp. 9816–72, doi:10.1021/acs.chemrev.1c00107.
short: J.A. Keith, V. Valentin Vassilev-Galindo, B. Cheng, S. Chmiela, M. Gastegger,
K.-R. Müller, A. Tkatchenko, Chemical Reviews 121 (2021) 9816–9872.
date_created: 2021-07-20T11:18:37Z
date_published: 2021-07-07T00:00:00Z
date_updated: 2023-05-08T11:31:03Z
day: '07'
doi: 10.1021/acs.chemrev.1c00107
extern: '1'
external_id:
arxiv:
- '2102.06321'
intvolume: ' 121'
issue: '16'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.1021/acs.chemrev.1c00107
month: '07'
oa: 1
oa_version: Published Version
page: 9816-9872
publication: Chemical Reviews
publication_identifier:
eissn:
- 1520-6890
issn:
- 0009-2665
publication_status: published
publisher: American Chemical Society
quality_controlled: '1'
scopus_import: '1'
status: public
title: Combining machine learning and computational chemistry for predictive insights
into chemical systems
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 121
year: '2021'
...
---
_id: '9658'
abstract:
- lang: eng
text: Macroscopic models of nucleation provide powerful tools for understanding
activated phase transition processes. These models do not provide atomistic insights
and can thus sometimes lack material-specific descriptions. Here, we provide a
comprehensive framework for constructing a continuum picture from an atomistic
simulation of homogeneous nucleation. We use this framework to determine the equilibrium
shape of the solid nucleus that forms inside bulk liquid for a Lennard-Jones potential.
From this shape, we then extract the anisotropy of the solid-liquid interfacial
free energy, by performing a reverse Wulff construction in the space of spherical
harmonic expansions. We find that the shape of the nucleus is nearly spherical
and that its anisotropy can be perfectly described using classical models.
article_number: '044103'
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Bingqing
full_name: Cheng, Bingqing
id: cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9
last_name: Cheng
orcid: 0000-0002-3584-9632
- first_name: Michele
full_name: Ceriotti, Michele
last_name: Ceriotti
- first_name: Gareth A.
full_name: Tribello, Gareth A.
last_name: Tribello
citation:
ama: Cheng B, Ceriotti M, Tribello GA. Classical nucleation theory predicts the
shape of the nucleus in homogeneous solidification. The Journal of Chemical
Physics. 2020;152(4). doi:10.1063/1.5134461
apa: Cheng, B., Ceriotti, M., & Tribello, G. A. (2020). Classical nucleation
theory predicts the shape of the nucleus in homogeneous solidification. The
Journal of Chemical Physics. AIP Publishing. https://doi.org/10.1063/1.5134461
chicago: Cheng, Bingqing, Michele Ceriotti, and Gareth A. Tribello. “Classical Nucleation
Theory Predicts the Shape of the Nucleus in Homogeneous Solidification.” The
Journal of Chemical Physics. AIP Publishing, 2020. https://doi.org/10.1063/1.5134461.
ieee: B. Cheng, M. Ceriotti, and G. A. Tribello, “Classical nucleation theory predicts
the shape of the nucleus in homogeneous solidification,” The Journal of Chemical
Physics, vol. 152, no. 4. AIP Publishing, 2020.
ista: Cheng B, Ceriotti M, Tribello GA. 2020. Classical nucleation theory predicts
the shape of the nucleus in homogeneous solidification. The Journal of Chemical
Physics. 152(4), 044103.
mla: Cheng, Bingqing, et al. “Classical Nucleation Theory Predicts the Shape of
the Nucleus in Homogeneous Solidification.” The Journal of Chemical Physics,
vol. 152, no. 4, 044103, AIP Publishing, 2020, doi:10.1063/1.5134461.
short: B. Cheng, M. Ceriotti, G.A. Tribello, The Journal of Chemical Physics 152
(2020).
date_created: 2021-07-15T07:22:24Z
date_published: 2020-01-31T00:00:00Z
date_updated: 2023-02-23T14:03:55Z
day: '31'
doi: 10.1063/1.5134461
extern: '1'
external_id:
arxiv:
- '1910.13481'
pmid:
- '32007057'
intvolume: ' 152'
issue: '4'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://pure.qub.ac.uk/en/publications/classical-nucleation-theory-predicts-the-shape-of-the-nucleus-in-homogeneous-solidification(56af848b-eee8-4e9b-93cf-667373e4a49b).html
month: '01'
oa: 1
oa_version: Submitted Version
pmid: 1
publication: The Journal of Chemical Physics
publication_identifier:
eissn:
- 1089-7690
issn:
- 0021-9606
publication_status: published
publisher: AIP Publishing
quality_controlled: '1'
scopus_import: '1'
status: public
title: Classical nucleation theory predicts the shape of the nucleus in homogeneous
solidification
type: journal_article
user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf
volume: 152
year: '2020'
...
---
_id: '9664'
abstract:
- lang: eng
text: Equilibrium molecular dynamics simulations, in combination with the Green-Kubo
(GK) method, have been extensively used to compute the thermal conductivity of
liquids. However, the GK method relies on an ambiguous definition of the microscopic
heat flux, which depends on how one chooses to distribute energies over atoms.
This ambiguity makes it problematic to employ the GK method for systems with nonpairwise
interactions. In this work, we show that the hydrodynamic description of thermally
driven density fluctuations can be used to obtain the thermal conductivity of
a bulk fluid unambiguously, thereby bypassing the need to define the heat flux.
We verify that, for a model fluid with only pairwise interactions, our method
yields estimates of thermal conductivity consistent with the GK approach. We apply
our approach to compute the thermal conductivity of a nonpairwise additive water
model at supercritical conditions, and of a liquid hydrogen system described by
a machine-learning interatomic potential, at 33 GPa and 2000 K.
article_number: '130602'
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Bingqing
full_name: Cheng, Bingqing
id: cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9
last_name: Cheng
orcid: 0000-0002-3584-9632
- first_name: Daan
full_name: Frenkel, Daan
last_name: Frenkel
citation:
ama: Cheng B, Frenkel D. Computing the heat conductivity of fluids from density
fluctuations. Physical Review Letters. 2020;125(13). doi:10.1103/physrevlett.125.130602
apa: Cheng, B., & Frenkel, D. (2020). Computing the heat conductivity of fluids
from density fluctuations. Physical Review Letters. American Physical Society.
https://doi.org/10.1103/physrevlett.125.130602
chicago: Cheng, Bingqing, and Daan Frenkel. “Computing the Heat Conductivity of
Fluids from Density Fluctuations.” Physical Review Letters. American Physical
Society, 2020. https://doi.org/10.1103/physrevlett.125.130602.
ieee: B. Cheng and D. Frenkel, “Computing the heat conductivity of fluids from density
fluctuations,” Physical Review Letters, vol. 125, no. 13. American Physical
Society, 2020.
ista: Cheng B, Frenkel D. 2020. Computing the heat conductivity of fluids from density
fluctuations. Physical Review Letters. 125(13), 130602.
mla: Cheng, Bingqing, and Daan Frenkel. “Computing the Heat Conductivity of Fluids
from Density Fluctuations.” Physical Review Letters, vol. 125, no. 13,
130602, American Physical Society, 2020, doi:10.1103/physrevlett.125.130602.
short: B. Cheng, D. Frenkel, Physical Review Letters 125 (2020).
date_created: 2021-07-15T12:15:14Z
date_published: 2020-09-25T00:00:00Z
date_updated: 2021-08-09T12:35:58Z
day: '25'
doi: 10.1103/physrevlett.125.130602
extern: '1'
external_id:
arxiv:
- '2005.07562'
pmid:
- '33034481'
intvolume: ' 125'
issue: '13'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2005.07562
month: '09'
oa: 1
oa_version: Preprint
pmid: 1
publication: Physical Review Letters
publication_identifier:
eissn:
- 1079-7114
issn:
- 0031-9007
publication_status: published
publisher: American Physical Society
quality_controlled: '1'
scopus_import: '1'
status: public
title: Computing the heat conductivity of fluids from density fluctuations
type: journal_article
user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf
volume: 125
year: '2020'
...
---
_id: '9666'
abstract:
- lang: eng
text: Predicting phase stabilities of crystal polymorphs is central to computational
materials science and chemistry. Such predictions are challenging because they
first require searching for potential energy minima and then performing arduous
free-energy calculations to account for entropic effects at finite temperatures.
Here, we develop a framework that facilitates such predictions by exploiting all
the information obtained from random searches of crystal structures. This framework
combines automated clustering, classification and visualisation of crystal structures
with machine-learning estimation of their enthalpy and entropy. We demonstrate
the framework on the technologically important system of TiO2, which has many
polymorphs, without relying on prior knowledge of known phases. We find a number
of new phases and predict the phase diagram and metastabilities of crystal polymorphs
at 1600 K, benchmarking the results against full free-energy calculations.
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Aleks
full_name: Reinhardt, Aleks
last_name: Reinhardt
- first_name: Chris J.
full_name: Pickard, Chris J.
last_name: Pickard
- first_name: Bingqing
full_name: Cheng, Bingqing
id: cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9
last_name: Cheng
orcid: 0000-0002-3584-9632
citation:
ama: Reinhardt A, Pickard CJ, Cheng B. Predicting the phase diagram of titanium
dioxide with random search and pattern recognition. Physical Chemistry Chemical
Physics. 2020;22(22):12697-12705. doi:10.1039/d0cp02513e
apa: Reinhardt, A., Pickard, C. J., & Cheng, B. (2020). Predicting the phase
diagram of titanium dioxide with random search and pattern recognition. Physical
Chemistry Chemical Physics. Royal Society of Chemistry. https://doi.org/10.1039/d0cp02513e
chicago: Reinhardt, Aleks, Chris J. Pickard, and Bingqing Cheng. “Predicting the
Phase Diagram of Titanium Dioxide with Random Search and Pattern Recognition.”
Physical Chemistry Chemical Physics. Royal Society of Chemistry, 2020.
https://doi.org/10.1039/d0cp02513e.
ieee: A. Reinhardt, C. J. Pickard, and B. Cheng, “Predicting the phase diagram of
titanium dioxide with random search and pattern recognition,” Physical Chemistry
Chemical Physics, vol. 22, no. 22. Royal Society of Chemistry, pp. 12697–12705,
2020.
ista: Reinhardt A, Pickard CJ, Cheng B. 2020. Predicting the phase diagram of titanium
dioxide with random search and pattern recognition. Physical Chemistry Chemical
Physics. 22(22), 12697–12705.
mla: Reinhardt, Aleks, et al. “Predicting the Phase Diagram of Titanium Dioxide
with Random Search and Pattern Recognition.” Physical Chemistry Chemical Physics,
vol. 22, no. 22, Royal Society of Chemistry, 2020, pp. 12697–705, doi:10.1039/d0cp02513e.
short: A. Reinhardt, C.J. Pickard, B. Cheng, Physical Chemistry Chemical Physics
22 (2020) 12697–12705.
date_created: 2021-07-15T12:37:27Z
date_published: 2020-06-14T00:00:00Z
date_updated: 2023-02-23T14:04:16Z
day: '14'
ddc:
- '530'
doi: 10.1039/d0cp02513e
extern: '1'
external_id:
arxiv:
- '1909.08934'
pmid:
- '32459228'
file:
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content_type: application/pdf
creator: asandaue
date_created: 2021-07-15T12:43:51Z
date_updated: 2021-07-15T12:43:51Z
file_id: '9667'
file_name: 202_PhysicalChemistryChemicalPhysics_Reinhardt.pdf
file_size: 3151206
relation: main_file
success: 1
file_date_updated: 2021-07-15T12:43:51Z
has_accepted_license: '1'
intvolume: ' 22'
issue: '22'
language:
- iso: eng
month: '06'
oa: 1
oa_version: Published Version
page: 12697-12705
pmid: 1
publication: Physical Chemistry Chemical Physics
publication_identifier:
eissn:
- 1463-9084
issn:
- 1463-9076
publication_status: published
publisher: Royal Society of Chemistry
quality_controlled: '1'
scopus_import: '1'
status: public
title: Predicting the phase diagram of titanium dioxide with random search and pattern
recognition
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/3.0/legalcode
name: Creative Commons Attribution 3.0 Unported (CC BY 3.0)
short: CC BY (3.0)
type: journal_article
user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf
volume: 22
year: '2020'
...
---
_id: '9671'
abstract:
- lang: eng
text: Water molecules can arrange into a liquid with complex hydrogen-bond networks
and at least 17 experimentally confirmed ice phases with enormous structural diversity.
It remains a puzzle how or whether this multitude of arrangements in different
phases of water are related. Here we investigate the structural similarities between
liquid water and a comprehensive set of 54 ice phases in simulations, by directly
comparing their local environments using general atomic descriptors, and also
by demonstrating that a machine-learning potential trained on liquid water alone
can predict the densities, lattice energies, and vibrational properties of the
ices. The finding that the local environments characterising the different ice
phases are found in water sheds light on the phase behavior of water, and rationalizes
the transferability of water models between different phases.
article_number: '5757'
article_processing_charge: No
article_type: original
author:
- first_name: Bartomeu
full_name: Monserrat, Bartomeu
last_name: Monserrat
- first_name: Jan Gerit
full_name: Brandenburg, Jan Gerit
last_name: Brandenburg
- first_name: Edgar A.
full_name: Engel, Edgar A.
last_name: Engel
- first_name: Bingqing
full_name: Cheng, Bingqing
id: cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9
last_name: Cheng
orcid: 0000-0002-3584-9632
citation:
ama: Monserrat B, Brandenburg JG, Engel EA, Cheng B. Liquid water contains the building
blocks of diverse ice phases. Nature Communications. 2020;11(1). doi:10.1038/s41467-020-19606-y
apa: Monserrat, B., Brandenburg, J. G., Engel, E. A., & Cheng, B. (2020). Liquid
water contains the building blocks of diverse ice phases. Nature Communications.
Springer Nature. https://doi.org/10.1038/s41467-020-19606-y
chicago: Monserrat, Bartomeu, Jan Gerit Brandenburg, Edgar A. Engel, and Bingqing
Cheng. “Liquid Water Contains the Building Blocks of Diverse Ice Phases.” Nature
Communications. Springer Nature, 2020. https://doi.org/10.1038/s41467-020-19606-y.
ieee: B. Monserrat, J. G. Brandenburg, E. A. Engel, and B. Cheng, “Liquid water
contains the building blocks of diverse ice phases,” Nature Communications,
vol. 11, no. 1. Springer Nature, 2020.
ista: Monserrat B, Brandenburg JG, Engel EA, Cheng B. 2020. Liquid water contains
the building blocks of diverse ice phases. Nature Communications. 11(1), 5757.
mla: Monserrat, Bartomeu, et al. “Liquid Water Contains the Building Blocks of Diverse
Ice Phases.” Nature Communications, vol. 11, no. 1, 5757, Springer Nature,
2020, doi:10.1038/s41467-020-19606-y.
short: B. Monserrat, J.G. Brandenburg, E.A. Engel, B. Cheng, Nature Communications
11 (2020).
date_created: 2021-07-15T14:01:35Z
date_published: 2020-11-13T00:00:00Z
date_updated: 2023-02-23T14:04:25Z
day: '13'
ddc:
- '530'
- '540'
doi: 10.1038/s41467-020-19606-y
extern: '1'
file:
- access_level: open_access
checksum: 1edd9b6d8fa791f8094d87bd6453955b
content_type: application/pdf
creator: asandaue
date_created: 2021-07-15T14:05:45Z
date_updated: 2021-07-15T14:05:45Z
file_id: '9672'
file_name: 2020_NatureCommunications_Monserrat.pdf
file_size: 1385954
relation: main_file
success: 1
file_date_updated: 2021-07-15T14:05:45Z
has_accepted_license: '1'
intvolume: ' 11'
issue: '1'
language:
- iso: eng
month: '11'
oa: 1
oa_version: Published Version
publication: Nature Communications
publication_identifier:
eissn:
- 2041-1723
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Liquid water contains the building blocks of diverse ice phases
tmp:
image: /images/cc_by.png
legal_code_url: https://creativecommons.org/licenses/by/4.0/legalcode
name: Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
short: CC BY (4.0)
type: journal_article
user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf
volume: 11
year: '2020'
...
---
_id: '9675'
abstract:
- lang: eng
text: The visualization of data is indispensable in scientific research, from the
early stages when human insight forms to the final step of communicating results.
In computational physics, chemistry and materials science, it can be as simple
as making a scatter plot or as straightforward as looking through the snapshots
of atomic positions manually. However, as a result of the "big data" revolution,
these conventional approaches are often inadequate. The widespread adoption of
high-throughput computation for materials discovery and the associated community-wide
repositories have given rise to data sets that contain an enormous number of compounds
and atomic configurations. A typical data set contains thousands to millions of
atomic structures, along with a diverse range of properties such as formation
energies, band gaps, or bioactivities.It would thus be desirable to have a data-driven
and automated framework for visualizing and analyzing such structural data sets.
The key idea is to construct a low-dimensional representation of the data, which
facilitates navigation, reveals underlying patterns, and helps to identify data
points with unusual attributes. Such data-intensive maps, often employing machine
learning methods, are appearing more and more frequently in the literature. However,
to the wider community, it is not always transparent how these maps are made and
how they should be interpreted. Furthermore, while these maps undoubtedly serve
a decorative purpose in academic publications, it is not always apparent what
extra information can be garnered from reading or making them.This Account attempts
to answer such questions. We start with a concise summary of the theory of representing
chemical environments, followed by the introduction of a simple yet practical
conceptual approach for generating structure maps in a generic and automated manner.
Such analysis and mapping is made nearly effortless by employing the newly developed
software tool ASAP. To showcase the applicability to a wide variety of systems
in chemistry and materials science, we provide several illustrative examples,
including crystalline and amorphous materials, interfaces, and organic molecules.
In these examples, the maps not only help to sift through large data sets but
also reveal hidden patterns that could be easily missed using conventional analyses.The
explosion in the amount of computed information in chemistry and materials science
has made visualization into a science in itself. Not only have we benefited from
exploiting these visualization methods in previous works, we also believe that
the automated mapping of data sets will in turn stimulate further creativity and
exploration, as well as ultimately feed back into future advances in the respective
fields.
article_processing_charge: No
article_type: original
author:
- first_name: Bingqing
full_name: Cheng, Bingqing
id: cbe3cda4-d82c-11eb-8dc7-8ff94289fcc9
last_name: Cheng
orcid: 0000-0002-3584-9632
- first_name: Ryan-Rhys
full_name: Griffiths, Ryan-Rhys
last_name: Griffiths
- first_name: Simon
full_name: Wengert, Simon
last_name: Wengert
- first_name: Christian
full_name: Kunkel, Christian
last_name: Kunkel
- first_name: Tamas
full_name: Stenczel, Tamas
last_name: Stenczel
- first_name: Bonan
full_name: Zhu, Bonan
last_name: Zhu
- first_name: Volker L.
full_name: Deringer, Volker L.
last_name: Deringer
- first_name: Noam
full_name: Bernstein, Noam
last_name: Bernstein
- first_name: Johannes T.
full_name: Margraf, Johannes T.
last_name: Margraf
- first_name: Karsten
full_name: Reuter, Karsten
last_name: Reuter
- first_name: Gabor
full_name: Csanyi, Gabor
last_name: Csanyi
citation:
ama: Cheng B, Griffiths R-R, Wengert S, et al. Mapping materials and molecules.
Accounts of Chemical Research. 2020;53(9):1981-1991. doi:10.1021/acs.accounts.0c00403
apa: Cheng, B., Griffiths, R.-R., Wengert, S., Kunkel, C., Stenczel, T., Zhu, B.,
… Csanyi, G. (2020). Mapping materials and molecules. Accounts of Chemical
Research. American Chemical Society. https://doi.org/10.1021/acs.accounts.0c00403
chicago: Cheng, Bingqing, Ryan-Rhys Griffiths, Simon Wengert, Christian Kunkel,
Tamas Stenczel, Bonan Zhu, Volker L. Deringer, et al. “Mapping Materials and Molecules.”
Accounts of Chemical Research. American Chemical Society, 2020. https://doi.org/10.1021/acs.accounts.0c00403.
ieee: B. Cheng et al., “Mapping materials and molecules,” Accounts of
Chemical Research, vol. 53, no. 9. American Chemical Society, pp. 1981–1991,
2020.
ista: Cheng B, Griffiths R-R, Wengert S, Kunkel C, Stenczel T, Zhu B, Deringer VL,
Bernstein N, Margraf JT, Reuter K, Csanyi G. 2020. Mapping materials and molecules.
Accounts of Chemical Research. 53(9), 1981–1991.
mla: Cheng, Bingqing, et al. “Mapping Materials and Molecules.” Accounts of Chemical
Research, vol. 53, no. 9, American Chemical Society, 2020, pp. 1981–91, doi:10.1021/acs.accounts.0c00403.
short: B. Cheng, R.-R. Griffiths, S. Wengert, C. Kunkel, T. Stenczel, B. Zhu, V.L.
Deringer, N. Bernstein, J.T. Margraf, K. Reuter, G. Csanyi, Accounts of Chemical
Research 53 (2020) 1981–1991.
date_created: 2021-07-16T06:25:53Z
date_published: 2020-08-14T00:00:00Z
date_updated: 2021-11-24T15:54:41Z
day: '14'
doi: 10.1021/acs.accounts.0c00403
extern: '1'
external_id:
pmid:
- '32794697'
intvolume: ' 53'
issue: '9'
language:
- iso: eng
month: '08'
oa_version: None
page: 1981-1991
pmid: 1
publication: Accounts of Chemical Research
publication_identifier:
eissn:
- 1520-4898
issn:
- 0001-4842
publication_status: published
publisher: American Chemical Society
quality_controlled: '1'
scopus_import: '1'
status: public
title: Mapping materials and molecules
type: journal_article
user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9
volume: 53
year: '2020'
...