One of the main goals and challenges of materials discovery is to find the best candidates for each interest property or application. Machine learning rises in this context to efficiently optimize this search, exploring the immense materials space, consisting of simultaneously the atomic, compositional, and structural spaces. Topological insulators, presenting symmetry-protected metallic edge states, are a promising class of materials for different applications. However, further development is limited by the scarcity of viable candidates. Here we present and discuss machine learning–accelerated strategies for searching the materials space for two-dimensional topological materials. We show the importance of detailed investigations of each machine learning component, leading to different results. Using recently created databases containing thousands of ab initio calculations of 2D materials, we train machine learning models capable of determining the electronic topology of materials, with an accuracy of over 90%. We can then generate and screen thousands of novel materials, efficiently predicting their topological character without the need for a priori structural knowledge. We discover 56 non-trivial materials, of which 17 are novel insulating candidates for further investigation, for which we corroborate their topological properties with density functional theory calculations. This strategy is 10× more efficient than the trial-and-error approach while a few orders of magnitude faster and is a proof of concept for guiding improved materials discovery search strategies.

中文翻译：

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用于材料发现的机器学习：二维拓扑绝缘体

材料发现的主要目标和挑战之一是为每个感兴趣的属性或应用找到最佳候选者。机器学习在这种情况下兴起，以有效地优化这种搜索，探索同时由原子、成分和结构空间组成的巨大材料空间。拓扑绝缘体，呈现对称保护的金属边缘状态，是一类具有不同应用前景的材料。然而，进一步的发展受到可行候选者的稀缺性的限制。在这里，我们提出并讨论了用于搜索二维拓扑材料的材料空间的机器学习加速策略。我们展示了对每个机器学习组件进行详细调查的重要性，从而导致了不同的结果。从2D 材料的ab initio计算，我们训练能够确定材料电子拓扑结构的机器学习模型，准确率超过 90%。然后，我们可以生成和筛选数千种新材料，有效地预测它们的拓扑特征，而无需先验结构知识。我们发现了 56 种重要材料，其中 17 种是新的绝缘材料，有待进一步研究，我们用密度泛函理论计算证实了它们的拓扑特性。该策略比试错法效率高 10 倍，速度快几个数量级，是指导改进材料发现搜索策略的概念证明。