---
_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. <i>The Journal of chemical physics</i>. 2022;156(7).
    doi:<a href="https://doi.org/10.1063/5.0079844">10.1063/5.0079844</a>
  apa: Lee, J. G., Pickard, C. J., &#38; Cheng, B. (2022). High-pressure phase behaviors
    of titanium dioxide revealed by a Δ-learning potential. <i>The Journal of Chemical
    Physics</i>. AIP Publishing. <a href="https://doi.org/10.1063/5.0079844">https://doi.org/10.1063/5.0079844</a>
  chicago: Lee, Jacob G., Chris J. Pickard, and Bingqing Cheng. “High-Pressure Phase
    Behaviors of Titanium Dioxide Revealed by a Δ-Learning Potential.” <i>The Journal
    of Chemical Physics</i>. AIP Publishing, 2022. <a href="https://doi.org/10.1063/5.0079844">https://doi.org/10.1063/5.0079844</a>.
  ieee: J. G. Lee, C. J. Pickard, and B. Cheng, “High-pressure phase behaviors of
    titanium dioxide revealed by a Δ-learning potential,” <i>The Journal of chemical
    physics</i>, 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.” <i>The Journal of Chemical Physics</i>, vol. 156,
    no. 7, 074106, AIP Publishing, 2022, doi:<a href="https://doi.org/10.1063/5.0079844">10.1063/5.0079844</a>.
  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: '7956'
abstract:
- lang: eng
  text: When short-range attractions are combined with long-range repulsions in colloidal
    particle systems, complex microphases can emerge. Here, we study a system of isotropic
    particles, which can form lamellar structures or a disordered fluid phase when
    temperature is varied. We show that, at equilibrium, the lamellar structure crystallizes,
    while out of equilibrium, the system forms a variety of structures at different
    shear rates and temperatures above melting. The shear-induced ordering is analyzed
    by means of principal component analysis and artificial neural networks, which
    are applied to data of reduced dimensionality. Our results reveal the possibility
    of inducing ordering by shear, potentially providing a feasible route to the fabrication
    of ordered lamellar structures from isotropic particles.
article_number: '204905'
article_processing_charge: No
article_type: original
arxiv: 1
author:
- first_name: J.
  full_name: Pȩkalski, J.
  last_name: Pȩkalski
- first_name: Wojciech
  full_name: Rzadkowski, Wojciech
  id: 48C55298-F248-11E8-B48F-1D18A9856A87
  last_name: Rzadkowski
  orcid: 0000-0002-1106-4419
- first_name: A. Z.
  full_name: Panagiotopoulos, A. Z.
  last_name: Panagiotopoulos
citation:
  ama: 'Pȩkalski J, Rzadkowski W, Panagiotopoulos AZ. Shear-induced ordering in systems
    with competing interactions: A machine learning study. <i>The Journal of chemical
    physics</i>. 2020;152(20). doi:<a href="https://doi.org/10.1063/5.0005194">10.1063/5.0005194</a>'
  apa: 'Pȩkalski, J., Rzadkowski, W., &#38; Panagiotopoulos, A. Z. (2020). Shear-induced
    ordering in systems with competing interactions: A machine learning study. <i>The
    Journal of Chemical Physics</i>. AIP Publishing. <a href="https://doi.org/10.1063/5.0005194">https://doi.org/10.1063/5.0005194</a>'
  chicago: 'Pȩkalski, J., Wojciech Rzadkowski, and A. Z. Panagiotopoulos. “Shear-Induced
    Ordering in Systems with Competing Interactions: A Machine Learning Study.” <i>The
    Journal of Chemical Physics</i>. AIP Publishing, 2020. <a href="https://doi.org/10.1063/5.0005194">https://doi.org/10.1063/5.0005194</a>.'
  ieee: 'J. Pȩkalski, W. Rzadkowski, and A. Z. Panagiotopoulos, “Shear-induced ordering
    in systems with competing interactions: A machine learning study,” <i>The Journal
    of chemical physics</i>, vol. 152, no. 20. AIP Publishing, 2020.'
  ista: 'Pȩkalski J, Rzadkowski W, Panagiotopoulos AZ. 2020. Shear-induced ordering
    in systems with competing interactions: A machine learning study. The Journal
    of chemical physics. 152(20), 204905.'
  mla: 'Pȩkalski, J., et al. “Shear-Induced Ordering in Systems with Competing Interactions:
    A Machine Learning Study.” <i>The Journal of Chemical Physics</i>, vol. 152, no.
    20, 204905, AIP Publishing, 2020, doi:<a href="https://doi.org/10.1063/5.0005194">10.1063/5.0005194</a>.'
  short: J. Pȩkalski, W. Rzadkowski, A.Z. Panagiotopoulos, The Journal of Chemical
    Physics 152 (2020).
date_created: 2020-06-14T22:00:49Z
date_published: 2020-05-29T00:00:00Z
date_updated: 2024-02-28T13:00:28Z
day: '29'
department:
- _id: MiLe
doi: 10.1063/5.0005194
ec_funded: 1
external_id:
  arxiv:
  - '2002.07294'
  isi:
  - '000537900300001'
intvolume: '       152'
isi: 1
issue: '20'
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://doi.org/10.1063/5.0005194
month: '05'
oa: 1
oa_version: Published Version
project:
- _id: 2564DBCA-B435-11E9-9278-68D0E5697425
  call_identifier: H2020
  grant_number: '665385'
  name: International IST Doctoral Program
publication: The Journal of chemical physics
publication_identifier:
  eissn:
  - '10897690'
publication_status: published
publisher: AIP Publishing
quality_controlled: '1'
related_material:
  record:
  - id: '10759'
    relation: dissertation_contains
    status: public
scopus_import: '1'
status: public
title: 'Shear-induced ordering in systems with competing interactions: A machine learning
  study'
type: journal_article
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
volume: 152
year: '2020'
...