Reinforcement learning in crystal structure prediction-Reference-Cited by-同舟云学术

Reinforcement learning in crystal structure prediction

Published:2023 Issue:6 Volume:2 Page:1831-1840
ISSN:2635-098X
Container-title:Digital Discovery
language:en
Short-container-title:Digital Discovery

Author:

Zamaraeva Elena¹^ORCID,Collins Christopher M.²^ORCID,Antypov Dmytro¹^ORCID,Gusev Vladimir V.¹³^ORCID,Savani Rahul³⁴^ORCID,Dyer Matthew S.¹²^ORCID,Darling George R.²^ORCID,Potapov Igor¹³^ORCID,Rosseinsky Matthew J.¹²^ORCID,Spirakis Paul G.¹³^ORCID

Affiliation:

1. Leverhulme Research Centre for Functional Materials Design, University of Liverpool, Materials Innovation Factory, Liverpool, UK

2. Department of Chemistry, University of Liverpool, Liverpool, UK

3. Department of Computer Science, University of Liverpool, Liverpool, UK

4. The Alan Turing Institute, London, UK

Abstract

Reinforcement learning accelerates crystal structure prediction by learning a dynamic policy to maximise the reward for exploring new crystal structures.

Funder

Leverhulme Trust

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2023/DD/D3DD00063J

Reference33 articles.

1. Crystal structure prediction from first principles

2. Large-Scale Computational Screening of Molecular Organic Semiconductors Using Crystal Structure Prediction

3. Structure prediction drives materials discovery

4. The Flexible Unit Structure Engine (FUSE) for probe structure-based composition prediction

5. New developments in evolutionary structure prediction algorithm USPEX

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Reinforcement Learning for Traversing Chemical Structure Space: Optimizing Transition States and Minimum Energy Paths of Molecules;The Journal of Physical Chemistry Letters;2024-01-03