npj: 层状材料设计—主动学习与贝叶斯优化

2019 年 1 月 15 日 知社学术圈

海归学者发起的公益学术平台

分享信息，整合资源

交流学术，偶尔风月

由过渡金属二硫属化合物单层垂直堆叠而成的异质结在光电和热电器件领域拥有巨大的应用潜力。发现用于特定领域的最优层状材料，需要先估算关键的材料特性，例如电子能带结构和热输运系数。然而，通过严格从头算方法搜索整个材料结构空间来筛选材料特性大大超过了目前计算资源的限制。此外，材料特性函数对其结构的依赖性通常很复杂，在没有收集大量数据的情况下，难以使用简单的统计程序开展预测。

南加州大学的Priya Vashishta领导的团队，提出了一个高斯过程回归模型，可基于异质结结构预测材料属性，同时提出了基于贝叶斯优化的主动学习模型，可基于最少的从头算工作量来有效地发现最佳异质结。基于上述方法他们找到了与光电和热电应用相关的最大带隙异质结或非常接近1.1 eV 带隙值的异质结。该研究开发的模型可用于预测任意的材料性质，且开发出相关软件都是开源的。

该文近期发表于npj Computational Materials 4: 74 (2018)，英文标题与摘要如下，点击左下角“阅读原文”可以自由获取论文PDF。

Active learning for accelerated design of layered materials

Lindsay Bassman, Pankaj Rajak, Rajiv K. Kalia, Aiichiro Nakano, Fei Sha, Jifeng Sun, David J. Singh, Muratahan Aykol, Patrick Huck, Kristin Persson & Priya Vashishta

Hetero-structures made from vertically stacked monolayers of transition metal dichalcogenides hold great potential for optoelectronic and thermoelectric devices. Discovery of the optimal layered material for specific applications necessitates the estimation of key material properties, such as electronic band structure and thermal transport coefficients. However, screening of material properties via brute force ab initio calculations of the entire material structure space exceeds the limits of current computing resources. Moreover, the functional dependence of material properties on the structures is often complicated, making simplistic statistical procedures for prediction difficult to employ without large amounts of data collection. Here, we present a Gaussian process regression model, which predicts material properties of an input hetero-structure, as well as an active learning model based on Bayesian optimization, which can efficiently discover the optimal hetero-structure using a minimal number of ab initio calculations. The electronic band gap, conduction/valence band dispersions, and thermoelectric performance are used as representative material properties for prediction and optimization. The Materials Project platform is used for electronic structure computation, while the BoltzTraP code is used to compute thermoelectric properties. Bayesian optimization is shown to significantly reduce the computational cost of discovering the optimal structure when compared with finding an optimal structure by building a regression model to predict material properties. The models can be used for predictions with respect to any material property and our software, including data preparation code based on the Python Materials Genomics (PyMatGen) library as well as python-based machine learning code, is available open source.