Hautier Geoffroy
Institute of Condensed Matter and Nanosciences (IMCN), Université Catholique de Louvain, Chemin des étoiles 8, bte L7.03.01, 1348, Louvain-la-Neuve, Belgium,
Top Curr Chem. 2014;345:139-79. doi: 10.1007/128_2013_486.
Predicting unknown inorganic compounds and their crystal structure is a critical step of high-throughput computational materials design and discovery. One way to achieve efficient compound prediction is to use data mining or machine learning methods. In this chapter we present a few algorithms for data mining compound prediction and their applications to different materials discovery problems. In particular, the patterns or correlations governing phase stability for experimental or computational inorganic compound databases are statistically learned and used to build probabilistic or regression models to identify novel compounds and their crystal structures. The stability of those compound candidates is then assessed using ab initio techniques. Finally, we report a few cases where data mining driven computational predictions were experimentally confirmed through inorganic synthesis.
预测未知无机化合物及其晶体结构是高通量计算材料设计与发现的关键步骤。实现高效化合物预测的一种方法是使用数据挖掘或机器学习方法。在本章中，我们介绍一些用于数据挖掘化合物预测的算法及其在不同材料发现问题中的应用。特别是，通过对实验或计算无机化合物数据库中控制相稳定性的模式或相关性进行统计学习，并用于构建概率或回归模型，以识别新型化合物及其晶体结构。然后使用从头算技术评估这些化合物候选物的稳定性。最后，我们报告了一些通过无机合成实验证实数据挖掘驱动的计算预测的案例。
