Issue 35, 2021

High-efficient ab initio Bayesian active learning method and applications in prediction of two-dimensional functional materials

Abstract

Beyond the conventional trial-and-error method, machine learning offers a great opportunity to accelerate the discovery of functional materials, but still often suffers from difficulties such as limited materials data and the unbalanced distribution of target properties. Here, we propose the ab initio Bayesian active learning method that combines active learning and high-throughput ab initio calculations to accelerate the prediction of desired functional materials with ultrahigh efficiency and accuracy. We apply it as an instance to a large family (3119) of two-dimensional hexagonal binary compounds with unbalanced materials properties, and accurately screen out the materials with maximal electric polarization and proper photovoltaic band gaps, respectively, whereas the computational costs are significantly reduced by only calculating a few tenths of the possible candidates in comparison with a random search. This approach shows the enormous advantages for the cases with unbalanced distribution of target properties. It can be readily applied to seek a broad range of advanced materials.

Graphical abstract: High-efficient ab initio Bayesian active learning method and applications in prediction of two-dimensional functional materials

Supplementary files

Article information

Article type
Paper
Submitted
16 Jūn. 2021
Accepted
09 Aug. 2021
First published
09 Aug. 2021

Nanoscale, 2021,13, 14694-14704

High-efficient ab initio Bayesian active learning method and applications in prediction of two-dimensional functional materials

X. Ma, H. Lyu, K. Hao, Z. Zhu, Q. Yan and G. Su, Nanoscale, 2021, 13, 14694 DOI: 10.1039/D1NR03886A

To request permission to reproduce material from this article, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements