Literature DB >> 24515753

Structure and stability prediction of compounds with evolutionary algorithms.

Benjamin C Revard1, William W Tipton, Richard G Hennig.   

Abstract

Crystal structure prediction is a long-standing challenge in the physical sciences. In recent years, much practical success has been had by framing it as a global optimization problem, leveraging the existence of increasingly robust and accurate free energy calculations. This optimization problem has often been solved using evolutionary algorithms (EAs). However, many choices are possible when designing an EA for structure prediction, and innovation in the field is ongoing. We review the current state of evolutionary algorithms for crystal structure and composition prediction and discuss the details of methodological and algorithmic choices. Finally, we review the application of these algorithms to many systems of practical and fundamental scientific interest.

Mesh:

Substances:

Year:  2014        PMID: 24515753     DOI: 10.1007/128_2013_489

Source DB:  PubMed          Journal:  Top Curr Chem        ISSN: 0340-1022


  3 in total

1.  Machine learning the metastable phase diagram of covalently bonded carbon.

Authors:  Srilok Srinivasan; Rohit Batra; Duan Luo; Troy Loeffler; Sukriti Manna; Henry Chan; Liuxiang Yang; Wenge Yang; Jianguo Wen; Pierre Darancet; Subramanian K R S Sankaranarayanan
Journal:  Nat Commun       Date:  2022-06-06       Impact factor: 17.694

2.  Predicting finite-temperature properties of crystalline carbon dioxide from first principles with quantitative accuracy.

Authors:  Yonaton N Heit; Kaushik D Nanda; Gregory J O Beran
Journal:  Chem Sci       Date:  2015-09-29       Impact factor: 9.825

3.  Ab initio prediction of the polymorph phase diagram for crystalline methanol.

Authors:  Ctirad Červinka; Gregory J O Beran
Journal:  Chem Sci       Date:  2018-04-16       Impact factor: 9.825

  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.