Multi-Objective Quality-Diversity for Crystal Structure Prediction
Hannah Janmohamed Marta Wolinska Shikha Surana Thomas Pierrot Aron Walsh Antoine Cully
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.