Predicting phase preferences of two-dimensional transition metal dichalcogenides using machine learning
Predicting phase preferences of two-dimensional transition metal dichalcogenides using machine learning
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DOI：
10.1103/physrevmaterials.6.094007
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发表时间：
2022-09-19
影响因子：
3.4
通讯作者：
Dev, Pratibha
中科院分区：
文献类型：
--
作者：
Kumar, Pankaj;Sharma, Vinit;Dev, Pratibha
二维过渡金属二硫属化物（TMD）可以采用几种可能的结构之一，最常见的是三角棱柱和八面体对称相。由于结构决定了电子性质，因此非常希望能够仅根据组成原子的知识来预测TMD的相位偏好，但这仍然是一个长期存在的问题。在这项研究中，我们应用高通量量子力学计算和机器学习算法来解决这个老问题。我们的分析提供了深入了解确定的物理化学因素，决定了一个TMD的相位偏好，识别和超越了早期研究人员在预测晶体结构所考虑的属性。了解这些潜在的生理化学因素不仅可以帮助我们合理化，而且可以准确地预测结构偏好。我们表明，机器学习算法是一种强大的工具，不仅可以用来寻找具有目标特性的新材料，还可以用来寻找元素属性与目标特性之间的联系，这些联系以前并不明显。
Two-dimensional transition metal dichalcogenides (TMDs) can adopt one of several possible structures, with the most common being the trigonal prismatic and octahedral symmetry phases. Since the structure determines the electronic properties, being able to predict phase preferences of TMDs from just the knowledge of the constituent atoms is highly desired, but has remained a long-standing problem. In this study, we applied high-throughput quantum mechanical computations with machine learning algorithms to solve this old problem. Our analysis provides insights into determining physiochemical factors that dictate the phase preference of a TMD, identifying and going beyond the attributes considered by earlier researchers in predicting crystal structures. A knowledge of these underlying physiochemical factors not only helps us to rationalize, but also to accurately predict structural preferences. We show that machine learning algorithms are powerful tools that can be used not only to find new materials with targeted properties, but also to find connections between elemental attributes and the target property/properties that were not previously obvious.