Multi-objective quality-diversity for crystal structure prediction
File(s)
Published version
Author(s)
Type
Conference Paper
Abstract
Crystal structures are indispensable across various domains, from batteries to solar cells, and extensive research has been dedicated to predicting their properties based on their atomic configurations. However, prevailing Crystal Structure Prediction methods focus on identifying the most stable solutions that lie at the global minimum of the energy function. This approach overlooks other potentially interesting materials that lie in neighbouring local minima and have different material properties such as conductivity or resistance to deformation. By contrast, Quality-Diversity algorithms provide a promising avenue for Crystal Structure Prediction as they aim to find a collection of high-performing solutions that have diverse characteristics. However, it may also be valuable to optimise for the stability of crystal structures alongside other objectives such as magnetism or thermoelectric efficiency. Therefore, in this work, we harness the power of Multi-Objective Quality-Diversity algorithms in order to find crystal structures which have diverse features and achieve different trade-offs of objectives. We analyse our approach on 5 crystal systems and demonstrate that it is not only able to re-discover known real-life structures, but also find promising new ones. Moreover, we propose a method for illuminating the objective space to gain an understanding of what trade-offs can be achieved.
Date Issued
2024-07-14
Date Acceptance
2024-03-21
Citation
GECCO '24: Proceedings of the Genetic and Evolutionary Computation Conference, 2024, pp.1273-1281
ISBN
9798400704949
Publisher
Association for Computing Machinery
Start Page
1273
End Page
1281
Journal / Book Title
GECCO '24: Proceedings of the Genetic and Evolutionary Computation Conference
Copyright Statement
© 2024 Copyright held by the owner/author(s). This work is licensed under a Creative Commons Attribution-Non-Derivatives International 4.0 License.
License URL
Identifier
10.1145/3638529.3654048
Source
The Genetic and Evolutionary Computation Conference
Publication Status
Published
Start Date
2024-07-14
Finish Date
2024-07-18
Coverage Spatial
Melbourne, Australia
Date Publish Online
2024-07-14