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arXiv 2107.07028cond-mat.mtrl-sci
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用于材料发现的机器学习:二维拓扑绝缘体

Machine learning for materials discovery: two-dimensional topological insulators

Gabriel R. Schleder, Bruno Focassio, Adalberto Fazzio

摘要:

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。机器学习在这种背景下兴起,以有效地优化这种搜索,探索巨大的材料空间,同时包括原子,成分和结构空间。拓扑绝缘体是一类具有良好应用前景的金属边缘态材料。然而,此外,发展受到缺乏可行候选人的限制。在这里,我们提出并讨论了机器学习加速策略,用于搜索二维拓扑材料的材料空间。我们展示了对每个机器学习组件进行详细调查的重要性,从而得出不同的结果。使用最近创建的包含数千个二维材料从头计算的数据库,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,而不需要先验结构知识。我们发现了56个非平凡的材料,其中17个新的绝缘候选人进一步调查,我们证实了他们的拓扑性质与密度泛函理论计算。该策略比试错法的效率高10 $\times$,同时快几个数量级,并且是用于指导改进的材料发现搜索策略的概念证明。

英文摘要:

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 novel insulating candidates for further investigation, for which we corroborate their topological properties with density functional theory calculations. This strategy is 10$\times$ more efficient than the trial-and-error approach while few orders of magnitude faster and is a proof of concept for guiding improved materials discovery search strategies.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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