Materials Screening for the Discovery of New Half-Heuslers: Machine Learning versus ab Initio Methods.
Materials Screening for the Discovery of New Half-Heuslers: Machine Learning versus ab Initio Methods.
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DOI：
10.1021/acs.jpcb.7b05296
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发表时间：
2017-06
期刊：
影响因子：
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通讯作者：
F. Legrain;J. Carrete;Ambroise van Roekeghem;G. Madsen;N. Mingo
中科院分区：
文献类型：
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作者：
F. Legrain;J. Carrete;Ambroise van Roekeghem;G. Madsen;N. Mingo
机器学习（ML）越来越成为寻找新功能化合物的有用工具。在这里，我们使用分类通过随机森林来预测半赫斯勒（HH）化合物的稳定性，仅使用实验报告的化合物作为训练集。交叉验证产生了一个很好的协议之间的分数的化合物分类为稳定和真正稳定的化合物在ICSD的实际分数。然后，采用ML模型筛选71178种不同的1：1：1组合物，产生481种可能的稳定候选物。HH化合物的预测稳定性从以前的三个高通量从头算研究的角度来看，替代ML方法进行了严格的分析。三个独立的从头算研究之间的不完全一致性，它们之间的ML预测表明，额外的因素以外的从头算相稳定性计算所考虑的可能是决定性的化合物的稳定性。这些因素可以包括组态熵和准谐波贡献。
Machine learning (ML) is increasingly becoming a helpful tool in the search for novel functional compounds. Here we use classification via random forests to predict the stability of half-Heusler (HH) compounds, using only experimentally reported compounds as a training set. Cross-validation yields an excellent agreement between the fraction of compounds classified as stable and the actual fraction of truly stable compounds in the ICSD. The ML model is then employed to screen 71 178 different 1:1:1 compositions, yielding 481 likely stable candidates. The predicted stability of HH compounds from three previous high-throughput ab initio studies is critically analyzed from the perspective of the alternative ML approach. The incomplete consistency among the three separate ab initio studies and between them and the ML predictions suggests that additional factors beyond those considered by ab initio phase stability calculations might be determinant to the stability of the compounds. Such factors can include configurational entropies and quasiharmonic contributions.