---
_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:
- access_level: open_access
checksum: 0a6872972b1b2e60f9095d39b01753fa
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
license: https://creativecommons.org/licenses/by/3.0/
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'
...
---
_id: '9685'
abstract:
- lang: eng
text: Hydrogen, the simplest and most abundant element in the Universe, develops
a remarkably complex behaviour upon compression^1. Since Wigner predicted the
dissociation and metallization of solid hydrogen at megabar pressures almost a
century ago^2, several efforts have been made to explain the many unusual properties
of dense hydrogen, including a rich and poorly understood solid polymorphism^1,3-5,
an anomalous melting line6 and the possible transition to a superconducting state^7.
Experiments at such extreme conditions are challenging and often lead to hard-to-interpret
and controversial observations, whereas theoretical investigations are constrained
by the huge computational cost of sufficiently accurate quantum mechanical calculations.
Here we present a theoretical study of the phase diagram of dense hydrogen that
uses machine learning to 'learn' potential-energy surfaces and interatomic forces
from reference calculations and then predict them at low computational cost, overcoming
length- and timescale limitations. We reproduce both the re-entrant melting behaviour
and the polymorphism of the solid phase. Simulations using our machine-learning-based
potentials provide evidence for a continuous molecular-to-atomic transition in
the liquid, with no first-order transition observed above the melting line. This
suggests a smooth transition between insulating and metallic layers in giant gas
planets, and reconciles existing discrepancies between experiments as a manifestation
of supercritical behaviour.
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: Guglielmo
full_name: Mazzola, Guglielmo
last_name: Mazzola
- first_name: Chris J.
full_name: Pickard, Chris J.
last_name: Pickard
- first_name: Michele
full_name: Ceriotti, Michele
last_name: Ceriotti
citation:
ama: Cheng B, Mazzola G, Pickard CJ, Ceriotti M. Evidence for supercritical behaviour
of high-pressure liquid hydrogen. Nature. 2020;585(7824):217-220. doi:10.1038/s41586-020-2677-y
apa: Cheng, B., Mazzola, G., Pickard, C. J., & Ceriotti, M. (2020). Evidence
for supercritical behaviour of high-pressure liquid hydrogen. Nature. Springer
Nature. https://doi.org/10.1038/s41586-020-2677-y
chicago: Cheng, Bingqing, Guglielmo Mazzola, Chris J. Pickard, and Michele Ceriotti.
“Evidence for Supercritical Behaviour of High-Pressure Liquid Hydrogen.” Nature.
Springer Nature, 2020. https://doi.org/10.1038/s41586-020-2677-y.
ieee: B. Cheng, G. Mazzola, C. J. Pickard, and M. Ceriotti, “Evidence for supercritical
behaviour of high-pressure liquid hydrogen,” Nature, vol. 585, no. 7824.
Springer Nature, pp. 217–220, 2020.
ista: Cheng B, Mazzola G, Pickard CJ, Ceriotti M. 2020. Evidence for supercritical
behaviour of high-pressure liquid hydrogen. Nature. 585(7824), 217–220.
mla: Cheng, Bingqing, et al. “Evidence for Supercritical Behaviour of High-Pressure
Liquid Hydrogen.” Nature, vol. 585, no. 7824, Springer Nature, 2020, pp.
217–20, doi:10.1038/s41586-020-2677-y.
short: B. Cheng, G. Mazzola, C.J. Pickard, M. Ceriotti, Nature 585 (2020) 217–220.
date_created: 2021-07-19T09:17:49Z
date_published: 2020-09-10T00:00:00Z
date_updated: 2021-08-09T12:38:01Z
day: '10'
doi: 10.1038/s41586-020-2677-y
extern: '1'
external_id:
arxiv:
- '1906.03341'
pmid:
- '32908269'
intvolume: ' 585'
issue: '7824'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1906.03341
month: '09'
oa: 1
oa_version: Preprint
page: 217-220
pmid: 1
publication: Nature
publication_identifier:
eissn:
- 1476-4687
issn:
- 0028-0836
publication_status: published
publisher: Springer Nature
quality_controlled: '1'
scopus_import: '1'
status: public
title: Evidence for supercritical behaviour of high-pressure liquid hydrogen
type: journal_article
user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf
volume: 585
year: '2020'
...
---
_id: '9699'
abstract:
- lang: eng
text: "We investigate the structural similarities between liquid water and 53 ices,
including 20 known crystalline phases. We base such similarity comparison on the
local environments that consist of atoms within a certain cutoff radius of a central
atom. We reveal that liquid water explores the local environments of the diverse
ice phases, by directly comparing the environments in these phases using general
atomic descriptors, and also by demonstrating that a machine-learning potential
trained on liquid water alone can predict the densities, the lattice energies,
and vibrational properties of the\r\nices. The finding that the local environments
characterising the different ice phases are found in water sheds light on water
phase behaviors, and rationalizes the transferability of water models between
different phases."
article_number: '2006.13316'
article_processing_charge: No
arxiv: 1
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. Extracting ice phases from
liquid water: Why a machine-learning water model generalizes so well. arXiv.
doi:10.48550/arXiv.2006.13316'
apa: 'Monserrat, B., Brandenburg, J. G., Engel, E. A., & Cheng, B. (n.d.). Extracting
ice phases from liquid water: Why a machine-learning water model generalizes so
well. arXiv. https://doi.org/10.48550/arXiv.2006.13316'
chicago: 'Monserrat, Bartomeu, Jan Gerit Brandenburg, Edgar A. Engel, and Bingqing
Cheng. “Extracting Ice Phases from Liquid Water: Why a Machine-Learning Water
Model Generalizes so Well.” ArXiv, n.d. https://doi.org/10.48550/arXiv.2006.13316.'
ieee: 'B. Monserrat, J. G. Brandenburg, E. A. Engel, and B. Cheng, “Extracting ice
phases from liquid water: Why a machine-learning water model generalizes so well,”
arXiv. .'
ista: 'Monserrat B, Brandenburg JG, Engel EA, Cheng B. Extracting ice phases from
liquid water: Why a machine-learning water model generalizes so well. arXiv, 2006.13316.'
mla: 'Monserrat, Bartomeu, et al. “Extracting Ice Phases from Liquid Water: Why
a Machine-Learning Water Model Generalizes so Well.” ArXiv, 2006.13316,
doi:10.48550/arXiv.2006.13316.'
short: B. Monserrat, J.G. Brandenburg, E.A. Engel, B. Cheng, ArXiv (n.d.).
date_created: 2021-07-20T11:25:15Z
date_published: 2020-06-23T00:00:00Z
date_updated: 2023-05-10T10:17:48Z
day: '23'
doi: 10.48550/arXiv.2006.13316
extern: '1'
external_id:
arxiv:
- '2006.13316'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/2006.13316
month: '06'
oa: 1
oa_version: Submitted Version
publication: arXiv
publication_status: submitted
status: public
title: 'Extracting ice phases from liquid water: Why a machine-learning water model
generalizes so well'
type: preprint
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2020'
...
---
_id: '9706'
abstract:
- lang: eng
text: 'Additional file 2: Supplementary Tables. The association of pre-adjusted
protein levels with biological and technical covariates. Protein levels were adjusted
for age, sex, array plate and four genetic principal components (population structure)
prior to analyses. Significant associations are emboldened. (Table S1). pQTLs
associated with inflammatory biomarker levels from Bayesian penalised regression
model (Posterior Inclusion Probability > 95%). (Table S2). All pQTLs associated
with inflammatory biomarker levels from ordinary least squares regression model
(P < 7.14 × 10− 10). (Table S3). Summary of lambda values relating to ordinary
least squares GWAS and EWAS performed on inflammatory protein levels (n = 70)
in Lothian Birth Cohort 1936 study. (Table S4). Conditionally significant pQTLs
associated with inflammatory biomarker levels from ordinary least squares regression
model (P < 7.14 × 10− 10). (Table S5). Comparison of variance explained by ordinary
least squares and Bayesian penalised regression models for concordantly identified
SNPs. (Table S6). Estimate of heritability for blood protein levels as well as
proportion of variance explained attributable to different prior mixtures. (Table
S7). Comparison of heritability estimates from Ahsan et al. (maximum likelihood)
and Hillary et al. (Bayesian penalised regression). (Table S8). List of concordant
SNPs identified by linear model and Bayesian penalised regression and whether
they have been previously identified as eQTLs. (Table S9). Bayesian tests of colocalisation
for cis pQTLs and cis eQTLs. (Table S10). Sherlock algorithm: Genes whose expression
are putatively associated with circulating inflammatory proteins that harbour
pQTLs. (Table S11). CpGs associated with inflammatory protein biomarkers as identified
by Bayesian model (Bayesian model; Posterior Inclusion Probability > 95%). (Table
S12). CpGs associated with inflammatory protein biomarkers as identified by linear
model (limma) at P < 5.14 × 10− 10. (Table S13). CpGs associated with inflammatory
protein biomarkers as identified by mixed linear model (OSCA) at P < 5.14 × 10− 10.
(Table S14). Estimate of variance explained for blood protein levels by DNA methylation
as well as proportion of explained attributable to different prior mixtures -
BayesR+. (Table S15). Comparison of variance in protein levels explained by genome-wide
DNA methylation data by mixed linear model (OSCA) and Bayesian penalised regression
model (BayesR+). (Table S16). Variance in circulating inflammatory protein biomarker
levels explained by common genetic and methylation data (joint and conditional
estimates from BayesR+). Ordered by combined variance explained by genetic and
epigenetic data - smallest to largest. Significant results from t-tests comparing
distributions for variance explained by methylation or genetics alone versus combined
estimate are emboldened. (Table S17). Genetic and epigenetic factors identified
by BayesR+ when conditioning on all SNPs and CpGs together. (Table S18). Mendelian
Randomisation analyses to assess whether proteins with concordantly identified
genetic signals are causally associated with Alzheimer’s disease risk. (Table
S19).'
article_processing_charge: No
author:
- first_name: Robert F.
full_name: Hillary, Robert F.
last_name: Hillary
- first_name: Daniel
full_name: Trejo-Banos, Daniel
last_name: Trejo-Banos
- first_name: Athanasios
full_name: Kousathanas, Athanasios
last_name: Kousathanas
- first_name: Daniel L.
full_name: McCartney, Daniel L.
last_name: McCartney
- first_name: Sarah E.
full_name: Harris, Sarah E.
last_name: Harris
- first_name: Anna J.
full_name: Stevenson, Anna J.
last_name: Stevenson
- first_name: Marion
full_name: Patxot, Marion
last_name: Patxot
- first_name: Sven Erik
full_name: Ojavee, Sven Erik
last_name: Ojavee
- first_name: Qian
full_name: Zhang, Qian
last_name: Zhang
- first_name: David C.
full_name: Liewald, David C.
last_name: Liewald
- first_name: Craig W.
full_name: Ritchie, Craig W.
last_name: Ritchie
- first_name: Kathryn L.
full_name: Evans, Kathryn L.
last_name: Evans
- first_name: Elliot M.
full_name: Tucker-Drob, Elliot M.
last_name: Tucker-Drob
- first_name: Naomi R.
full_name: Wray, Naomi R.
last_name: Wray
- first_name: 'Allan F. '
full_name: 'McRae, Allan F. '
last_name: McRae
- first_name: Peter M.
full_name: Visscher, Peter M.
last_name: Visscher
- first_name: Ian J.
full_name: Deary, Ian J.
last_name: Deary
- first_name: Matthew Richard
full_name: Robinson, Matthew Richard
id: E5D42276-F5DA-11E9-8E24-6303E6697425
last_name: Robinson
orcid: 0000-0001-8982-8813
- first_name: 'Riccardo E. '
full_name: 'Marioni, Riccardo E. '
last_name: Marioni
citation:
ama: Hillary RF, Trejo-Banos D, Kousathanas A, et al. Additional file 2 of multi-method
genome- and epigenome-wide studies of inflammatory protein levels in healthy older
adults. 2020. doi:10.6084/m9.figshare.12629697.v1
apa: Hillary, R. F., Trejo-Banos, D., Kousathanas, A., McCartney, D. L., Harris,
S. E., Stevenson, A. J., … Marioni, R. E. (2020). Additional file 2 of multi-method
genome- and epigenome-wide studies of inflammatory protein levels in healthy older
adults. Springer Nature. https://doi.org/10.6084/m9.figshare.12629697.v1
chicago: Hillary, Robert F., Daniel Trejo-Banos, Athanasios Kousathanas, Daniel
L. McCartney, Sarah E. Harris, Anna J. Stevenson, Marion Patxot, et al. “Additional
File 2 of Multi-Method Genome- and Epigenome-Wide Studies of Inflammatory Protein
Levels in Healthy Older Adults.” Springer Nature, 2020. https://doi.org/10.6084/m9.figshare.12629697.v1.
ieee: R. F. Hillary et al., “Additional file 2 of multi-method genome- and
epigenome-wide studies of inflammatory protein levels in healthy older adults.”
Springer Nature, 2020.
ista: Hillary RF, Trejo-Banos D, Kousathanas A, McCartney DL, Harris SE, Stevenson
AJ, Patxot M, Ojavee SE, Zhang Q, Liewald DC, Ritchie CW, Evans KL, Tucker-Drob
EM, Wray NR, McRae AF, Visscher PM, Deary IJ, Robinson MR, Marioni RE. 2020. Additional
file 2 of multi-method genome- and epigenome-wide studies of inflammatory protein
levels in healthy older adults, Springer Nature, 10.6084/m9.figshare.12629697.v1.
mla: Hillary, Robert F., et al. Additional File 2 of Multi-Method Genome- and
Epigenome-Wide Studies of Inflammatory Protein Levels in Healthy Older Adults.
Springer Nature, 2020, doi:10.6084/m9.figshare.12629697.v1.
short: R.F. Hillary, D. Trejo-Banos, A. Kousathanas, D.L. McCartney, S.E. Harris,
A.J. Stevenson, M. Patxot, S.E. Ojavee, Q. Zhang, D.C. Liewald, C.W. Ritchie,
K.L. Evans, E.M. Tucker-Drob, N.R. Wray, A.F. McRae, P.M. Visscher, I.J. Deary,
M.R. Robinson, R.E. Marioni, (2020).
date_created: 2021-07-23T08:59:15Z
date_published: 2020-07-09T00:00:00Z
date_updated: 2023-08-22T07:55:36Z
day: '09'
department:
- _id: MaRo
doi: 10.6084/m9.figshare.12629697.v1
has_accepted_license: '1'
main_file_link:
- open_access: '1'
url: https://doi.org/10.6084/m9.figshare.12629697.v1
month: '07'
oa: 1
oa_version: Published Version
other_data_license: CC0 + CC BY (4.0)
publisher: Springer Nature
related_material:
record:
- id: '8133'
relation: used_in_publication
status: public
status: public
title: Additional file 2 of multi-method genome- and epigenome-wide studies of inflammatory
protein levels in healthy older adults
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: research_data_reference
user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf
year: '2020'
...
---
_id: '9708'
abstract:
- lang: eng
text: This research data supports 'Hard antinodal gap revealed by quantum oscillations
in the pseudogap regime of underdoped high-Tc superconductors'. A Readme file
for plotting each figure is provided.
article_processing_charge: No
author:
- first_name: Mate
full_name: Hartstein, Mate
last_name: Hartstein
- first_name: Yu-Te
full_name: Hsu, Yu-Te
last_name: Hsu
- first_name: Kimberly A
full_name: Modic, Kimberly A
id: 13C26AC0-EB69-11E9-87C6-5F3BE6697425
last_name: Modic
orcid: 0000-0001-9760-3147
- first_name: Juan
full_name: Porras, Juan
last_name: Porras
- first_name: Toshinao
full_name: Loew, Toshinao
last_name: Loew
- first_name: Matthieu
full_name: Le Tacon, Matthieu
last_name: Le Tacon
- first_name: Huakun
full_name: Zuo, Huakun
last_name: Zuo
- first_name: Jinhua
full_name: Wang, Jinhua
last_name: Wang
- first_name: Zengwei
full_name: Zhu, Zengwei
last_name: Zhu
- first_name: Mun
full_name: Chan, Mun
last_name: Chan
- first_name: Ross
full_name: McDonald, Ross
last_name: McDonald
- first_name: Gilbert
full_name: Lonzarich, Gilbert
last_name: Lonzarich
- first_name: Bernhard
full_name: Keimer, Bernhard
last_name: Keimer
- first_name: Suchitra
full_name: Sebastian, Suchitra
last_name: Sebastian
- first_name: Neil
full_name: Harrison, Neil
last_name: Harrison
citation:
ama: Hartstein M, Hsu Y-T, Modic KA, et al. Accompanying dataset for “Hard antinodal
gap revealed by quantum oscillations in the pseudogap regime of underdoped high-Tc
superconductors.” 2020. doi:10.17863/cam.50169
apa: Hartstein, M., Hsu, Y.-T., Modic, K. A., Porras, J., Loew, T., Le Tacon, M.,
… Harrison, N. (2020). Accompanying dataset for “Hard antinodal gap revealed by
quantum oscillations in the pseudogap regime of underdoped high-Tc superconductors.”
Apollo - University of Cambridge. https://doi.org/10.17863/cam.50169
chicago: Hartstein, Mate, Yu-Te Hsu, Kimberly A Modic, Juan Porras, Toshinao Loew,
Matthieu Le Tacon, Huakun Zuo, et al. “Accompanying Dataset for ‘Hard Antinodal
Gap Revealed by Quantum Oscillations in the Pseudogap Regime of Underdoped High-Tc
Superconductors.’” Apollo - University of Cambridge, 2020. https://doi.org/10.17863/cam.50169.
ieee: M. Hartstein et al., “Accompanying dataset for ‘Hard antinodal gap
revealed by quantum oscillations in the pseudogap regime of underdoped high-Tc
superconductors.’” Apollo - University of Cambridge, 2020.
ista: Hartstein M, Hsu Y-T, Modic KA, Porras J, Loew T, Le Tacon M, Zuo H, Wang
J, Zhu Z, Chan M, McDonald R, Lonzarich G, Keimer B, Sebastian S, Harrison N.
2020. Accompanying dataset for ‘Hard antinodal gap revealed by quantum oscillations
in the pseudogap regime of underdoped high-Tc superconductors’, Apollo - University
of Cambridge, 10.17863/cam.50169.
mla: Hartstein, Mate, et al. Accompanying Dataset for “Hard Antinodal Gap Revealed
by Quantum Oscillations in the Pseudogap Regime of Underdoped High-Tc Superconductors.”
Apollo - University of Cambridge, 2020, doi:10.17863/cam.50169.
short: M. Hartstein, Y.-T. Hsu, K.A. Modic, J. Porras, T. Loew, M. Le Tacon, H.
Zuo, J. Wang, Z. Zhu, M. Chan, R. McDonald, G. Lonzarich, B. Keimer, S. Sebastian,
N. Harrison, (2020).
date_created: 2021-07-23T10:00:35Z
date_published: 2020-05-29T00:00:00Z
date_updated: 2023-08-21T07:06:48Z
day: '29'
department:
- _id: KiMo
doi: 10.17863/cam.50169
has_accepted_license: '1'
main_file_link:
- open_access: '1'
url: https://doi.org/10.17863/CAM.50169
month: '05'
oa: 1
oa_version: Published Version
publisher: Apollo - University of Cambridge
related_material:
record:
- id: '7942'
relation: used_in_publication
status: public
status: public
title: Accompanying dataset for 'Hard antinodal gap revealed by quantum oscillations
in the pseudogap regime of underdoped high-Tc superconductors'
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: research_data_reference
user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf
year: '2020'
...
---
_id: '9713'
abstract:
- lang: eng
text: Additional analyses of the trajectories
article_processing_charge: No
author:
- first_name: Chitrak
full_name: Gupta, Chitrak
last_name: Gupta
- first_name: Umesh
full_name: Khaniya, Umesh
last_name: Khaniya
- first_name: Chun Kit
full_name: Chan, Chun Kit
last_name: Chan
- first_name: Francois
full_name: Dehez, Francois
last_name: Dehez
- first_name: Mrinal
full_name: Shekhar, Mrinal
last_name: Shekhar
- first_name: M.R.
full_name: Gunner, M.R.
last_name: Gunner
- first_name: Leonid A
full_name: Sazanov, Leonid A
id: 338D39FE-F248-11E8-B48F-1D18A9856A87
last_name: Sazanov
orcid: 0000-0002-0977-7989
- first_name: Christophe
full_name: Chipot, Christophe
last_name: Chipot
- first_name: Abhishek
full_name: Singharoy, Abhishek
last_name: Singharoy
citation:
ama: Gupta C, Khaniya U, Chan CK, et al. Supporting information. 2020. doi:10.1021/jacs.9b13450.s001
apa: Gupta, C., Khaniya, U., Chan, C. K., Dehez, F., Shekhar, M., Gunner, M. R.,
… Singharoy, A. (2020). Supporting information. American Chemical Society . https://doi.org/10.1021/jacs.9b13450.s001
chicago: Gupta, Chitrak, Umesh Khaniya, Chun Kit Chan, Francois Dehez, Mrinal Shekhar,
M.R. Gunner, Leonid A Sazanov, Christophe Chipot, and Abhishek Singharoy. “Supporting
Information.” American Chemical Society , 2020. https://doi.org/10.1021/jacs.9b13450.s001.
ieee: C. Gupta et al., “Supporting information.” American Chemical Society
, 2020.
ista: Gupta C, Khaniya U, Chan CK, Dehez F, Shekhar M, Gunner MR, Sazanov LA, Chipot
C, Singharoy A. 2020. Supporting information, American Chemical Society , 10.1021/jacs.9b13450.s001.
mla: Gupta, Chitrak, et al. Supporting Information. American Chemical Society
, 2020, doi:10.1021/jacs.9b13450.s001.
short: C. Gupta, U. Khaniya, C.K. Chan, F. Dehez, M. Shekhar, M.R. Gunner, L.A.
Sazanov, C. Chipot, A. Singharoy, (2020).
date_created: 2021-07-23T12:02:39Z
date_published: 2020-05-20T00:00:00Z
date_updated: 2023-08-22T07:49:38Z
day: '20'
department:
- _id: LeSa
doi: 10.1021/jacs.9b13450.s001
month: '05'
oa_version: Published Version
publisher: 'American Chemical Society '
related_material:
record:
- id: '8040'
relation: used_in_publication
status: public
status: public
title: Supporting information
type: research_data_reference
user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf
year: '2020'
...
---
_id: '9750'
abstract:
- lang: eng
text: Tension of the actomyosin cell cortex plays a key role in determining cell-cell
contact growth and size. The level of cortical tension outside of the cell-cell
contact, when pulling at the contact edge, scales with the total size to which
a cell-cell contact can grow1,2. Here we show in zebrafish primary germ layer
progenitor cells that this monotonic relationship only applies to a narrow range
of cortical tension increase, and that above a critical threshold, contact size
inversely scales with cortical tension. This switch from cortical tension increasing
to decreasing progenitor cell-cell contact size is caused by cortical tension
promoting E-cadherin anchoring to the actomyosin cytoskeleton, thereby increasing
clustering and stability of E-cadherin at the contact. Once tension-mediated E-cadherin
stabilization at the contact exceeds a critical threshold level, the rate by which
the contact expands in response to pulling forces from the cortex sharply drops,
leading to smaller contacts at physiologically relevant timescales of contact
formation. Thus, the activity of cortical tension in expanding cell-cell contact
size is limited by tension stabilizing E-cadherin-actin complexes at the contact.
acknowledged_ssus:
- _id: Bio
- _id: EM-Fac
- _id: SSU
acknowledgement: We would like to thank Edouard Hannezo for discussions, Shayan Shami
Pour and Daniel Capek for help with data analysis, Vanessa Barone and other members
of the Heisenberg laboratory for thoughtful discussions and comments on the manuscript.
We also thank Jack Merrin for preparing the microwells, and the Scientific Service
Units at IST Austria, specifically Bioimaging and Electron Microscopy, and the Zebrafish
Facility for continuous support. We acknowledge Hitoshi Morita for the kind gift
of VinculinB-GFP plasmid. This research was supported by an ERC Advanced Grant (MECSPEC)
to C.-P.H, EMBO Long Term grant (ALTF 187-2013) to M.S and IST Fellow Marie-Curie
COFUND No. P_IST_EU01 to J.S.
article_processing_charge: No
author:
- first_name: Jana
full_name: Slovakova, Jana
id: 30F3F2F0-F248-11E8-B48F-1D18A9856A87
last_name: Slovakova
- first_name: Mateusz K
full_name: Sikora, Mateusz K
id: 2F74BCDE-F248-11E8-B48F-1D18A9856A87
last_name: Sikora
- first_name: Silvia
full_name: Caballero Mancebo, Silvia
id: 2F1E1758-F248-11E8-B48F-1D18A9856A87
last_name: Caballero Mancebo
orcid: 0000-0002-5223-3346
- first_name: Gabriel
full_name: Krens, Gabriel
id: 2B819732-F248-11E8-B48F-1D18A9856A87
last_name: Krens
orcid: 0000-0003-4761-5996
- first_name: Walter
full_name: Kaufmann, Walter
id: 3F99E422-F248-11E8-B48F-1D18A9856A87
last_name: Kaufmann
orcid: 0000-0001-9735-5315
- first_name: Karla
full_name: Huljev, Karla
id: 44C6F6A6-F248-11E8-B48F-1D18A9856A87
last_name: Huljev
- first_name: Carl-Philipp J
full_name: Heisenberg, Carl-Philipp J
id: 39427864-F248-11E8-B48F-1D18A9856A87
last_name: Heisenberg
orcid: 0000-0002-0912-4566
citation:
ama: Slovakova J, Sikora MK, Caballero Mancebo S, et al. Tension-dependent stabilization
of E-cadherin limits cell-cell contact expansion. bioRxiv. 2020. doi:10.1101/2020.11.20.391284
apa: Slovakova, J., Sikora, M. K., Caballero Mancebo, S., Krens, G., Kaufmann, W.,
Huljev, K., & Heisenberg, C.-P. J. (2020). Tension-dependent stabilization
of E-cadherin limits cell-cell contact expansion. bioRxiv. Cold Spring
Harbor Laboratory. https://doi.org/10.1101/2020.11.20.391284
chicago: Slovakova, Jana, Mateusz K Sikora, Silvia Caballero Mancebo, Gabriel Krens,
Walter Kaufmann, Karla Huljev, and Carl-Philipp J Heisenberg. “Tension-Dependent
Stabilization of E-Cadherin Limits Cell-Cell Contact Expansion.” BioRxiv.
Cold Spring Harbor Laboratory, 2020. https://doi.org/10.1101/2020.11.20.391284.
ieee: J. Slovakova et al., “Tension-dependent stabilization of E-cadherin
limits cell-cell contact expansion,” bioRxiv. Cold Spring Harbor Laboratory,
2020.
ista: Slovakova J, Sikora MK, Caballero Mancebo S, Krens G, Kaufmann W, Huljev K,
Heisenberg C-PJ. 2020. Tension-dependent stabilization of E-cadherin limits cell-cell
contact expansion. bioRxiv, 10.1101/2020.11.20.391284.
mla: Slovakova, Jana, et al. “Tension-Dependent Stabilization of E-Cadherin Limits
Cell-Cell Contact Expansion.” BioRxiv, Cold Spring Harbor Laboratory, 2020,
doi:10.1101/2020.11.20.391284.
short: J. Slovakova, M.K. Sikora, S. Caballero Mancebo, G. Krens, W. Kaufmann, K.
Huljev, C.-P.J. Heisenberg, BioRxiv (2020).
date_created: 2021-07-29T11:29:50Z
date_published: 2020-11-20T00:00:00Z
date_updated: 2024-03-25T23:30:10Z
day: '20'
department:
- _id: CaHe
- _id: EM-Fac
- _id: Bio
doi: 10.1101/2020.11.20.391284
ec_funded: 1
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://doi.org/10.1101/2020.11.20.391284
month: '11'
oa: 1
oa_version: Preprint
page: '41'
project:
- _id: 25681D80-B435-11E9-9278-68D0E5697425
call_identifier: FP7
grant_number: '291734'
name: International IST Postdoc Fellowship Programme
- _id: 260F1432-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '742573'
name: Interaction and feedback between cell mechanics and fate specification in
vertebrate gastrulation
- _id: 2521E28E-B435-11E9-9278-68D0E5697425
grant_number: 187-2013
name: Modulation of adhesion function in cell-cell contact formation by cortical
tension
publication: bioRxiv
publication_status: published
publisher: Cold Spring Harbor Laboratory
related_material:
record:
- id: '10766'
relation: later_version
status: public
- id: '9623'
relation: dissertation_contains
status: public
status: public
title: Tension-dependent stabilization of E-cadherin limits cell-cell contact expansion
type: preprint
user_id: 8b945eb4-e2f2-11eb-945a-df72226e66a9
year: '2020'
...
---
_id: '9776'
article_processing_charge: No
author:
- first_name: Rok
full_name: Grah, Rok
id: 483E70DE-F248-11E8-B48F-1D18A9856A87
last_name: Grah
orcid: 0000-0003-2539-3560
- first_name: Tamar
full_name: Friedlander, Tamar
last_name: Friedlander
citation:
ama: Grah R, Friedlander T. Supporting information. 2020. doi:10.1371/journal.pcbi.1007642.s001
apa: Grah, R., & Friedlander, T. (2020). Supporting information. Public Library
of Science. https://doi.org/10.1371/journal.pcbi.1007642.s001
chicago: Grah, Rok, and Tamar Friedlander. “Supporting Information.” Public Library
of Science, 2020. https://doi.org/10.1371/journal.pcbi.1007642.s001.
ieee: R. Grah and T. Friedlander, “Supporting information.” Public Library of Science,
2020.
ista: Grah R, Friedlander T. 2020. Supporting information, Public Library of Science,
10.1371/journal.pcbi.1007642.s001.
mla: Grah, Rok, and Tamar Friedlander. Supporting Information. Public Library
of Science, 2020, doi:10.1371/journal.pcbi.1007642.s001.
short: R. Grah, T. Friedlander, (2020).
date_created: 2021-08-06T07:15:04Z
date_published: 2020-02-25T00:00:00Z
date_updated: 2023-08-18T06:47:47Z
day: '25'
department:
- _id: GaTk
doi: 10.1371/journal.pcbi.1007642.s001
month: '02'
oa_version: Published Version
publisher: Public Library of Science
related_material:
record:
- id: '7569'
relation: used_in_publication
status: public
status: public
title: Supporting information
type: research_data_reference
user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf
year: '2020'
...
---
_id: '9777'
article_processing_charge: No
author:
- first_name: Rok
full_name: Grah, Rok
id: 483E70DE-F248-11E8-B48F-1D18A9856A87
last_name: Grah
orcid: 0000-0003-2539-3560
- first_name: Tamar
full_name: Friedlander, Tamar
last_name: Friedlander
citation:
ama: Grah R, Friedlander T. Maximizing crosstalk. 2020. doi:10.1371/journal.pcbi.1007642.s002
apa: Grah, R., & Friedlander, T. (2020). Maximizing crosstalk. Public Library
of Science. https://doi.org/10.1371/journal.pcbi.1007642.s002
chicago: Grah, Rok, and Tamar Friedlander. “Maximizing Crosstalk.” Public Library
of Science, 2020. https://doi.org/10.1371/journal.pcbi.1007642.s002.
ieee: R. Grah and T. Friedlander, “Maximizing crosstalk.” Public Library of Science,
2020.
ista: Grah R, Friedlander T. 2020. Maximizing crosstalk, Public Library of Science,
10.1371/journal.pcbi.1007642.s002.
mla: Grah, Rok, and Tamar Friedlander. Maximizing Crosstalk. Public Library
of Science, 2020, doi:10.1371/journal.pcbi.1007642.s002.
short: R. Grah, T. Friedlander, (2020).
date_created: 2021-08-06T07:21:51Z
date_published: 2020-02-25T00:00:00Z
date_updated: 2023-09-12T11:02:25Z
day: '25'
department:
- _id: GaTk
doi: 10.1371/journal.pcbi.1007642.s002
main_file_link:
- open_access: '1'
url: https://doi.org/10.1371/journal.pcbi.1007642.s002
month: '02'
oa: 1
oa_version: None
publisher: Public Library of Science
related_material:
record:
- id: '7569'
relation: used_in_publication
status: public
status: public
title: Maximizing crosstalk
type: research_data_reference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2020'
...
---
_id: '9779'
article_processing_charge: No
author:
- first_name: Rok
full_name: Grah, Rok
id: 483E70DE-F248-11E8-B48F-1D18A9856A87
last_name: Grah
orcid: 0000-0003-2539-3560
- first_name: Tamar
full_name: Friedlander, Tamar
last_name: Friedlander
citation:
ama: Grah R, Friedlander T. Distribution of crosstalk values. 2020. doi:10.1371/journal.pcbi.1007642.s003
apa: Grah, R., & Friedlander, T. (2020). Distribution of crosstalk values. Public
Library of Science. https://doi.org/10.1371/journal.pcbi.1007642.s003
chicago: Grah, Rok, and Tamar Friedlander. “Distribution of Crosstalk Values.” Public
Library of Science, 2020. https://doi.org/10.1371/journal.pcbi.1007642.s003.
ieee: R. Grah and T. Friedlander, “Distribution of crosstalk values.” Public Library
of Science, 2020.
ista: Grah R, Friedlander T. 2020. Distribution of crosstalk values, Public Library
of Science, 10.1371/journal.pcbi.1007642.s003.
mla: Grah, Rok, and Tamar Friedlander. Distribution of Crosstalk Values.
Public Library of Science, 2020, doi:10.1371/journal.pcbi.1007642.s003.
short: R. Grah, T. Friedlander, (2020).
date_created: 2021-08-06T07:24:37Z
date_published: 2020-02-25T00:00:00Z
date_updated: 2023-08-18T06:47:47Z
day: '25'
department:
- _id: GaTk
doi: 10.1371/journal.pcbi.1007642.s003
month: '02'
oa_version: Published Version
publisher: Public Library of Science
related_material:
record:
- id: '7569'
relation: research_data
status: public
status: public
title: Distribution of crosstalk values
type: research_data_reference
user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf
year: '2020'
...
---
_id: '9780'
abstract:
- lang: eng
text: "PADREV : 4,4'-dimethoxy[1,1'-biphenyl]-2,2',5,5'-tetrol\r\nSpace Group: C
2 (5), Cell: a 24.488(16)Å b 5.981(4)Å c 3.911(3)Å, α 90° β 91.47(3)° γ 90°"
article_processing_charge: No
author:
- first_name: Werner
full_name: Schlemmer, Werner
last_name: Schlemmer
- first_name: Philipp
full_name: Nothdurft, Philipp
last_name: Nothdurft
- first_name: Alina
full_name: Petzold, Alina
last_name: Petzold
- first_name: Gisbert
full_name: Riess, Gisbert
last_name: Riess
- first_name: Philipp
full_name: Frühwirt, Philipp
last_name: Frühwirt
- first_name: Max
full_name: Schmallegger, Max
last_name: Schmallegger
- first_name: Georg
full_name: Gescheidt-Demner, Georg
last_name: Gescheidt-Demner
- first_name: Roland
full_name: Fischer, Roland
last_name: Fischer
- first_name: Stefan Alexander
full_name: Freunberger, Stefan Alexander
id: A8CA28E6-CE23-11E9-AD2D-EC27E6697425
last_name: Freunberger
orcid: 0000-0003-2902-5319
- first_name: Wolfgang
full_name: Kern, Wolfgang
last_name: Kern
- first_name: Stefan
full_name: Spirk, Stefan
last_name: Spirk
citation:
ama: 'Schlemmer W, Nothdurft P, Petzold A, et al. CCDC 1991959: Experimental Crystal
Structure Determination. 2020. doi:10.5517/ccdc.csd.cc24vsrk'
apa: 'Schlemmer, W., Nothdurft, P., Petzold, A., Riess, G., Frühwirt, P., Schmallegger,
M., … Spirk, S. (2020). CCDC 1991959: Experimental Crystal Structure Determination.
CCDC. https://doi.org/10.5517/ccdc.csd.cc24vsrk'
chicago: 'Schlemmer, Werner, Philipp Nothdurft, Alina Petzold, Gisbert Riess, Philipp
Frühwirt, Max Schmallegger, Georg Gescheidt-Demner, et al. “CCDC 1991959: Experimental
Crystal Structure Determination.” CCDC, 2020. https://doi.org/10.5517/ccdc.csd.cc24vsrk.'
ieee: 'W. Schlemmer et al., “CCDC 1991959: Experimental Crystal Structure
Determination.” CCDC, 2020.'
ista: 'Schlemmer W, Nothdurft P, Petzold A, Riess G, Frühwirt P, Schmallegger M,
Gescheidt-Demner G, Fischer R, Freunberger SA, Kern W, Spirk S. 2020. CCDC 1991959:
Experimental Crystal Structure Determination, CCDC, 10.5517/ccdc.csd.cc24vsrk.'
mla: 'Schlemmer, Werner, et al. CCDC 1991959: Experimental Crystal Structure
Determination. CCDC, 2020, doi:10.5517/ccdc.csd.cc24vsrk.'
short: W. Schlemmer, P. Nothdurft, A. Petzold, G. Riess, P. Frühwirt, M. Schmallegger,
G. Gescheidt-Demner, R. Fischer, S.A. Freunberger, W. Kern, S. Spirk, (2020).
date_created: 2021-08-06T07:41:07Z
date_published: 2020-03-22T00:00:00Z
date_updated: 2023-09-05T16:03:47Z
day: '22'
department:
- _id: StFr
doi: 10.5517/ccdc.csd.cc24vsrk
main_file_link:
- open_access: '1'
url: https://dx.doi.org/10.5517/ccdc.csd.cc24vsrk
month: '03'
oa: 1
oa_version: Published Version
publisher: CCDC
related_material:
record:
- id: '8329'
relation: used_in_publication
status: public
status: public
title: 'CCDC 1991959: Experimental Crystal Structure Determination'
type: research_data_reference
user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf
year: '2020'
...
---
_id: '9781'
abstract:
- lang: eng
text: We consider the Pekar functional on a ball in ℝ3. We prove uniqueness of minimizers,
and a quadratic lower bound in terms of the distance to the minimizer. The latter
follows from nondegeneracy of the Hessian at the minimum.
acknowledgement: We are grateful for the hospitality at the Mittag-Leffler Institute,
where part of this work has been done. The work of the authors was supported by
the European Research Council (ERC)under the European Union's Horizon 2020 research
and innovation programme grant 694227.
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: Dario
full_name: Feliciangeli, Dario
id: 41A639AA-F248-11E8-B48F-1D18A9856A87
last_name: Feliciangeli
orcid: 0000-0003-0754-8530
- first_name: Robert
full_name: Seiringer, Robert
id: 4AFD0470-F248-11E8-B48F-1D18A9856A87
last_name: Seiringer
orcid: 0000-0002-6781-0521
citation:
ama: Feliciangeli D, Seiringer R. Uniqueness and nondegeneracy of minimizers of
the Pekar functional on a ball. SIAM Journal on Mathematical Analysis.
2020;52(1):605-622. doi:10.1137/19m126284x
apa: Feliciangeli, D., & Seiringer, R. (2020). Uniqueness and nondegeneracy
of minimizers of the Pekar functional on a ball. SIAM Journal on Mathematical
Analysis. Society for Industrial & Applied Mathematics . https://doi.org/10.1137/19m126284x
chicago: Feliciangeli, Dario, and Robert Seiringer. “Uniqueness and Nondegeneracy
of Minimizers of the Pekar Functional on a Ball.” SIAM Journal on Mathematical
Analysis. Society for Industrial & Applied Mathematics , 2020. https://doi.org/10.1137/19m126284x.
ieee: D. Feliciangeli and R. Seiringer, “Uniqueness and nondegeneracy of minimizers
of the Pekar functional on a ball,” SIAM Journal on Mathematical Analysis,
vol. 52, no. 1. Society for Industrial & Applied Mathematics , pp. 605–622,
2020.
ista: Feliciangeli D, Seiringer R. 2020. Uniqueness and nondegeneracy of minimizers
of the Pekar functional on a ball. SIAM Journal on Mathematical Analysis. 52(1),
605–622.
mla: Feliciangeli, Dario, and Robert Seiringer. “Uniqueness and Nondegeneracy of
Minimizers of the Pekar Functional on a Ball.” SIAM Journal on Mathematical
Analysis, vol. 52, no. 1, Society for Industrial & Applied Mathematics
, 2020, pp. 605–22, doi:10.1137/19m126284x.
short: D. Feliciangeli, R. Seiringer, SIAM Journal on Mathematical Analysis 52 (2020)
605–622.
date_created: 2021-08-06T07:34:16Z
date_published: 2020-02-12T00:00:00Z
date_updated: 2023-09-07T13:30:11Z
day: '12'
ddc:
- '510'
department:
- _id: RoSe
doi: 10.1137/19m126284x
ec_funded: 1
external_id:
arxiv:
- '1904.08647 '
isi:
- '000546967700022'
has_accepted_license: '1'
intvolume: ' 52'
isi: 1
issue: '1'
keyword:
- Applied Mathematics
- Computational Mathematics
- Analysis
language:
- iso: eng
license: https://creativecommons.org/licenses/by-nc-nd/4.0/
main_file_link:
- open_access: '1'
url: https://arxiv.org/abs/1904.08647
month: '02'
oa: 1
oa_version: Preprint
page: 605-622
project:
- _id: 25C6DC12-B435-11E9-9278-68D0E5697425
call_identifier: H2020
grant_number: '694227'
name: Analysis of quantum many-body systems
publication: SIAM Journal on Mathematical Analysis
publication_identifier:
eissn:
- 1095-7154
issn:
- 0036-1410
publication_status: published
publisher: 'Society for Industrial & Applied Mathematics '
quality_controlled: '1'
related_material:
record:
- id: '9733'
relation: dissertation_contains
status: public
scopus_import: '1'
status: public
title: Uniqueness and nondegeneracy of minimizers of the Pekar functional on a ball
tmp:
image: /images/cc_by_nc_nd.png
legal_code_url: https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode
name: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
(CC BY-NC-ND 4.0)
short: CC BY-NC-ND (4.0)
type: journal_article
user_id: 4359f0d1-fa6c-11eb-b949-802e58b17ae8
volume: 52
year: '2020'
...
---
_id: '9798'
abstract:
- lang: eng
text: Fitness interactions between mutations can influence a population’s evolution
in many different ways. While epistatic effects are difficult to measure precisely,
important information is captured by the mean and variance of log fitnesses for
individuals carrying different numbers of mutations. We derive predictions for
these quantities from a class of simple fitness landscapes, based on models of
optimizing selection on quantitative traits. We also explore extensions to the
models, including modular pleiotropy, variable effect sizes, mutational bias and
maladaptation of the wild type. We illustrate our approach by reanalysing a large
dataset of mutant effects in a yeast snoRNA. Though characterized by some large
epistatic effects, these data give a good overall fit to the non-epistatic null
model, suggesting that epistasis might have limited influence on the evolutionary
dynamics in this system. We also show how the amount of epistasis depends on both
the underlying fitness landscape and the distribution of mutations, and so is
expected to vary in consistent ways between new mutations, standing variation
and fixed mutations.
article_processing_charge: No
author:
- first_name: Christelle
full_name: Fraisse, Christelle
id: 32DF5794-F248-11E8-B48F-1D18A9856A87
last_name: Fraisse
orcid: 0000-0001-8441-5075
- first_name: John J.
full_name: Welch, John J.
last_name: Welch
citation:
ama: Fraisse C, Welch JJ. Simulation code for Fig S2 from the distribution of epistasis
on simple fitness landscapes. 2020. doi:10.6084/m9.figshare.7957472.v1
apa: Fraisse, C., & Welch, J. J. (2020). Simulation code for Fig S2 from the
distribution of epistasis on simple fitness landscapes. Royal Society of London.
https://doi.org/10.6084/m9.figshare.7957472.v1
chicago: Fraisse, Christelle, and John J. Welch. “Simulation Code for Fig S2 from
the Distribution of Epistasis on Simple Fitness Landscapes.” Royal Society of
London, 2020. https://doi.org/10.6084/m9.figshare.7957472.v1.
ieee: C. Fraisse and J. J. Welch, “Simulation code for Fig S2 from the distribution
of epistasis on simple fitness landscapes.” Royal Society of London, 2020.
ista: Fraisse C, Welch JJ. 2020. Simulation code for Fig S2 from the distribution
of epistasis on simple fitness landscapes, Royal Society of London, 10.6084/m9.figshare.7957472.v1.
mla: Fraisse, Christelle, and John J. Welch. Simulation Code for Fig S2 from
the Distribution of Epistasis on Simple Fitness Landscapes. Royal Society
of London, 2020, doi:10.6084/m9.figshare.7957472.v1.
short: C. Fraisse, J.J. Welch, (2020).
date_created: 2021-08-06T11:18:15Z
date_published: 2020-10-15T00:00:00Z
date_updated: 2023-08-25T10:34:41Z
day: '15'
department:
- _id: BeVi
- _id: NiBa
doi: 10.6084/m9.figshare.7957472.v1
main_file_link:
- open_access: '1'
url: https://doi.org/10.6084/m9.figshare.7957472.v1
month: '10'
oa: 1
oa_version: Published Version
publisher: Royal Society of London
related_material:
record:
- id: '6467'
relation: used_in_publication
status: public
status: public
title: Simulation code for Fig S2 from the distribution of epistasis on simple fitness
landscapes
type: research_data_reference
user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf
year: '2020'
...
---
_id: '9799'
abstract:
- lang: eng
text: Fitness interactions between mutations can influence a population’s evolution
in many different ways. While epistatic effects are difficult to measure precisely,
important information is captured by the mean and variance of log fitnesses for
individuals carrying different numbers of mutations. We derive predictions for
these quantities from a class of simple fitness landscapes, based on models of
optimizing selection on quantitative traits. We also explore extensions to the
models, including modular pleiotropy, variable effect sizes, mutational bias and
maladaptation of the wild type. We illustrate our approach by reanalysing a large
dataset of mutant effects in a yeast snoRNA. Though characterized by some large
epistatic effects, these data give a good overall fit to the non-epistatic null
model, suggesting that epistasis might have limited influence on the evolutionary
dynamics in this system. We also show how the amount of epistasis depends on both
the underlying fitness landscape and the distribution of mutations, and so is
expected to vary in consistent ways between new mutations, standing variation
and fixed mutations.
article_processing_charge: No
author:
- first_name: Christelle
full_name: Fraisse, Christelle
id: 32DF5794-F248-11E8-B48F-1D18A9856A87
last_name: Fraisse
orcid: 0000-0001-8441-5075
- first_name: John J.
full_name: Welch, John J.
last_name: Welch
citation:
ama: Fraisse C, Welch JJ. Simulation code for Fig S1 from the distribution of epistasis
on simple fitness landscapes. 2020. doi:10.6084/m9.figshare.7957469.v1
apa: Fraisse, C., & Welch, J. J. (2020). Simulation code for Fig S1 from the
distribution of epistasis on simple fitness landscapes. Royal Society of London.
https://doi.org/10.6084/m9.figshare.7957469.v1
chicago: Fraisse, Christelle, and John J. Welch. “Simulation Code for Fig S1 from
the Distribution of Epistasis on Simple Fitness Landscapes.” Royal Society of
London, 2020. https://doi.org/10.6084/m9.figshare.7957469.v1.
ieee: C. Fraisse and J. J. Welch, “Simulation code for Fig S1 from the distribution
of epistasis on simple fitness landscapes.” Royal Society of London, 2020.
ista: Fraisse C, Welch JJ. 2020. Simulation code for Fig S1 from the distribution
of epistasis on simple fitness landscapes, Royal Society of London, 10.6084/m9.figshare.7957469.v1.
mla: Fraisse, Christelle, and John J. Welch. Simulation Code for Fig S1 from
the Distribution of Epistasis on Simple Fitness Landscapes. Royal Society
of London, 2020, doi:10.6084/m9.figshare.7957469.v1.
short: C. Fraisse, J.J. Welch, (2020).
date_created: 2021-08-06T11:26:57Z
date_published: 2020-10-15T00:00:00Z
date_updated: 2023-08-25T10:34:41Z
day: '15'
department:
- _id: BeVi
- _id: NiBa
doi: 10.6084/m9.figshare.7957469.v1
main_file_link:
- open_access: '1'
url: https://doi.org/10.6084/m9.figshare.7957469.v1
month: '10'
oa: 1
oa_version: Published Version
publisher: Royal Society of London
related_material:
record:
- id: '6467'
relation: used_in_publication
status: public
status: public
title: Simulation code for Fig S1 from the distribution of epistasis on simple fitness
landscapes
type: research_data_reference
user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf
year: '2020'
...
---
_id: '9814'
abstract:
- lang: eng
text: Data and mathematica notebooks for plotting figures from Language learning
with communication between learners
article_processing_charge: No
author:
- first_name: Rasmus
full_name: Ibsen-Jensen, Rasmus
id: 3B699956-F248-11E8-B48F-1D18A9856A87
last_name: Ibsen-Jensen
orcid: 0000-0003-4783-0389
- first_name: Josef
full_name: Tkadlec, Josef
id: 3F24CCC8-F248-11E8-B48F-1D18A9856A87
last_name: Tkadlec
orcid: 0000-0002-1097-9684
- first_name: Krishnendu
full_name: Chatterjee, Krishnendu
id: 2E5DCA20-F248-11E8-B48F-1D18A9856A87
last_name: Chatterjee
orcid: 0000-0002-4561-241X
- first_name: Martin
full_name: Nowak, Martin
last_name: Nowak
citation:
ama: Ibsen-Jensen R, Tkadlec J, Chatterjee K, Nowak M. Data and mathematica notebooks
for plotting figures from language learning with communication between learners
from language acquisition with communication between learners. 2020. doi:10.6084/m9.figshare.5973013.v1
apa: Ibsen-Jensen, R., Tkadlec, J., Chatterjee, K., & Nowak, M. (2020). Data
and mathematica notebooks for plotting figures from language learning with communication
between learners from language acquisition with communication between learners.
Royal Society. https://doi.org/10.6084/m9.figshare.5973013.v1
chicago: Ibsen-Jensen, Rasmus, Josef Tkadlec, Krishnendu Chatterjee, and Martin
Nowak. “Data and Mathematica Notebooks for Plotting Figures from Language Learning
with Communication between Learners from Language Acquisition with Communication
between Learners.” Royal Society, 2020. https://doi.org/10.6084/m9.figshare.5973013.v1.
ieee: R. Ibsen-Jensen, J. Tkadlec, K. Chatterjee, and M. Nowak, “Data and mathematica
notebooks for plotting figures from language learning with communication between
learners from language acquisition with communication between learners.” Royal
Society, 2020.
ista: Ibsen-Jensen R, Tkadlec J, Chatterjee K, Nowak M. 2020. Data and mathematica
notebooks for plotting figures from language learning with communication between
learners from language acquisition with communication between learners, Royal
Society, 10.6084/m9.figshare.5973013.v1.
mla: Ibsen-Jensen, Rasmus, et al. Data and Mathematica Notebooks for Plotting
Figures from Language Learning with Communication between Learners from Language
Acquisition with Communication between Learners. Royal Society, 2020, doi:10.6084/m9.figshare.5973013.v1.
short: R. Ibsen-Jensen, J. Tkadlec, K. Chatterjee, M. Nowak, (2020).
date_created: 2021-08-06T13:09:57Z
date_published: 2020-10-15T00:00:00Z
date_updated: 2023-10-18T06:36:00Z
day: '15'
department:
- _id: KrCh
doi: 10.6084/m9.figshare.5973013.v1
main_file_link:
- open_access: '1'
url: https://doi.org/10.6084/m9.figshare.5973013.v1
month: '10'
oa: 1
oa_version: Published Version
publisher: Royal Society
related_material:
record:
- id: '198'
relation: used_in_publication
status: public
status: public
title: Data and mathematica notebooks for plotting figures from language learning
with communication between learners from language acquisition with communication
between learners
type: research_data_reference
user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf
year: '2020'
...
---
_id: '9878'
article_processing_charge: No
author:
- first_name: Chitrak
full_name: Gupta, Chitrak
last_name: Gupta
- first_name: Umesh
full_name: Khaniya, Umesh
last_name: Khaniya
- first_name: Chun Kit
full_name: Chan, Chun Kit
last_name: Chan
- first_name: Francois
full_name: Dehez, Francois
last_name: Dehez
- first_name: Mrinal
full_name: Shekhar, Mrinal
last_name: Shekhar
- first_name: M.R.
full_name: Gunner, M.R.
last_name: Gunner
- first_name: Leonid A
full_name: Sazanov, Leonid A
id: 338D39FE-F248-11E8-B48F-1D18A9856A87
last_name: Sazanov
orcid: 0000-0002-0977-7989
- first_name: Christophe
full_name: Chipot, Christophe
last_name: Chipot
- first_name: Abhishek
full_name: Singharoy, Abhishek
last_name: Singharoy
citation:
ama: Gupta C, Khaniya U, Chan CK, et al. Movies. 2020. doi:10.1021/jacs.9b13450.s002
apa: Gupta, C., Khaniya, U., Chan, C. K., Dehez, F., Shekhar, M., Gunner, M. R.,
… Singharoy, A. (2020). Movies. American Chemical Society. https://doi.org/10.1021/jacs.9b13450.s002
chicago: Gupta, Chitrak, Umesh Khaniya, Chun Kit Chan, Francois Dehez, Mrinal Shekhar,
M.R. Gunner, Leonid A Sazanov, Christophe Chipot, and Abhishek Singharoy. “Movies.”
American Chemical Society, 2020. https://doi.org/10.1021/jacs.9b13450.s002.
ieee: C. Gupta et al., “Movies.” American Chemical Society, 2020.
ista: Gupta C, Khaniya U, Chan CK, Dehez F, Shekhar M, Gunner MR, Sazanov LA, Chipot
C, Singharoy A. 2020. Movies, American Chemical Society, 10.1021/jacs.9b13450.s002.
mla: Gupta, Chitrak, et al. Movies. American Chemical Society, 2020, doi:10.1021/jacs.9b13450.s002.
short: C. Gupta, U. Khaniya, C.K. Chan, F. Dehez, M. Shekhar, M.R. Gunner, L.A.
Sazanov, C. Chipot, A. Singharoy, (2020).
date_created: 2021-08-11T09:18:54Z
date_published: 2020-05-20T00:00:00Z
date_updated: 2023-08-22T07:49:38Z
day: '20'
department:
- _id: LeSa
doi: 10.1021/jacs.9b13450.s002
month: '05'
oa_version: Published Version
publisher: American Chemical Society
related_material:
record:
- id: '8040'
relation: used_in_publication
status: public
status: public
title: Movies
type: research_data_reference
user_id: 6785fbc1-c503-11eb-8a32-93094b40e1cf
year: '2020'
...