npj Computational Materials

Active learning for accelerated design of layered materials (主动学习加速层状材料的设计)
Lindsay Bassman, Pankaj Rajak, Rajiv K. Kalia, Aiichiro Nakano, Fei Sha, Jifeng Sun, David J. Singh, Muratahan Aykol, Patrick Huck, Kristin Persson & Priya Vashishta
npj Computational Materials 4:74 (2018)
doi:s41524-018-0129-0
Published online:10 December 2018
Abstract| Full Text | PDF OPEN

摘要: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。要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，通过严格从头计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。本研究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。我们选取电子带隙、导带/价带色散关系和热电性能作为代表性的材料特性开展预测和优化。采用Materials Project平台计算电子结构，BoltzTraP程序用于计算热电性能。与构建回归模型相比，采用贝叶斯优化预测最优材料结构可以显著降低计算成本。本研究开发的模型可用于预测任意的材料性质，并且开发的软件（基于Python材料基因组学（PyMatGen）数据库的数据准备程序以及python机器学习程序）都是开源的。　　

Abstract: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.

Editorial Summary

Materials design: Bayesian optimization (材料设计：贝叶斯优化)

使用贝叶斯优化（BO）可以高精度的预测材料性能。南加州大学的Priya Vashishta领导的团队，开发了一种高斯回归模型，能够预测过渡金属二硫属化合物单层堆叠构成的三层范德华异质结的带隙值和热电性质。进一步，采用BO模型可以基于最少的从头计算数据量识别最佳异质结。他们采用BO模型计算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。发现BO识别近乎最优材料组合的概率很高，并能显着降低使用回归模型发现理想结构的计算成本。

High accuracy predictions of materials properties can be obtained using Bayesian optimization (BO). A team led by Priya Vashishta at University of Southern California developed a Gaussian regression model capable of predicting the band gap value and thermoelectric properties of three-layered van der Waals heterostructures of transition metal dichalcogenides. A BO model further allowed identification of optimal heterostructures using a minimal number of ab initio calculations. BO models were computed to find either heterostructures with maximum band gap or heterostructures with a band gap value closest to 1.1？eV, relevant for optoelectronic and thermoelectric applications. BO was found to identify nearly optimal materials configurations with high probability, whilst significantly reducing the computational cost of discovering ideal structures using regression models.