NEAILGMar 25, 2024

Multi-Objective Quality-Diversity for Crystal Structure Prediction

arXiv:2403.17164v29 citationsh-index: 8GECCO
Originality Synthesis-oriented
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This work addresses the problem of discovering diverse and multi-objective crystal structures for materials science applications, representing an incremental advancement by applying an existing algorithmic framework to a new domain.

The paper tackles the limitation of existing Crystal Structure Prediction methods that focus only on stable solutions by using Multi-Objective Quality-Diversity algorithms to find diverse crystal structures with trade-offs in objectives like stability and magnetism, demonstrating the approach on 5 crystal systems to rediscover known structures and find new ones.

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.

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