The identification of materials with exceptional properties is an essential objective to enable technological progress. We propose the application of \textit{Quality-Diversity} algorithms to the field of crystal structure prediction. The objective of these algorithms is to identify a diverse set of high-performing solutions, which has been successful in a range of fields such as robotics, architecture and aeronautical engineering. As these methods rely on a high number of evaluations, we employ machine-learning surrogate models to compute the interatomic potential and material properties that are used to guide optimisation. Consequently, we also show the value of using neural networks to model crystal properties and enable the identification of novel composition--structure combinations. In this work, we specifically study the application of the MAP-Elites algorithm to predict polymorphs of TiO$_2$. We rediscover the known ground state, in addition to a set of other polymorphs with distinct properties. We validate our method for C, SiO$_2$ and SiC systems, where we show that the algorithm can uncover multiple local minima with distinct electronic and mechanical properties.

翻译：具有优异性质的材料识别是推动技术进步的关键目标。我们提出将\textit{质量-多样性}算法应用于晶体结构预测领域。这类算法旨在识别一组多样性优异的高性能解，已在机器人学、建筑学和航空工程等多个领域取得成功。由于这些方法依赖于大量评估，我们采用机器学习代理模型来计算用于指导优化的原子间势和材料性质。因此，我们还展示了利用神经网络建模晶体性质及识别新型组成-结构组合的价值。本研究中，我们专门研究了MAP-Elites算法在预测TiO$_2$多晶型中的应用。除发现已知基态结构外，我们还获得了一组具有不同性质的多晶型。我们通过C、SiO$_2$和SiC体系验证了该方法，结果表明该算法能够揭示多个具有独特电子和力学性质的局部极小值点。