Machine Learning

The results of crystal structure prediction studies provide a rich source of information on structure-stability relationships and, when coupled with property simulations, the relationships between solid state structure and function. This is an attractive application of machine learning methods, as a means of accelerating the methods and improving reliability of the results. Unsupervised learning approaches are also being developed to identify structure in crystal structure prediction datasets, which can help in interpreting results and maximising what we learn from each study.

Rebecca J Clements, Joshua Dickman, Jay Johal, Jennie Martin, Joseph Glover, Graeme M Day  

MRS Bulletin, 47, 1054–1062 (2022) 

Qiang Zhu, Jay Johal, Daniel E. Widdowson, Zhongfu Pang, Boyu Li, Christopher M. Kane, Vitaliy Kurlin, Graeme M. Day, Marc A. Little, and Andrew I. Cooper

Journal of the American Cheical Society, 144, 9893-9901 (2022).

Martins Balodis, Manuel Cordova, Albert Hofstetter, Graeme M. Day, Lyndon Emsley

Journal of the American Chemical Society, 144, 16, 7215-7223 (2022).

 Chengxi Zhao, Linjiang Chen, Yu Che, Zhongfu Pang, Xiaofeng Wu, Yunxiang Lu, Honglai Liu, Graeme M. Day and Andrew I. Cooper

Nature Communications, (2021), 12, article number 817.

 Olga Egorova, Roohollah Hafizi, David C Woods and Graeme M. Day

J. Phys. Chem. A., Phys. Chem. A, 124, 8065-8078 (2020)

Jack Yang, Sandip De, Josh E. Campbell, Sean Li, Michele Ceriotti, Graeme M. Day

Chemistry of Materials, 30, p.4361-4371 (2018)

David McDonagh, Chris-Kriton Skylaris and Graeme M. Day

Journal of Chemical Theory and Computation, 15, 2743-2758 (2019)

Felix Musil, Sandip De, Jack Yang, Joshua E. Campbell, Graeme M. Day and Michele Ceriotti

Chemical Science, 9, 1289-1300 (2018)