Simulation and design of energy materials accelerated by machine learning,Wiley Interdisciplinary Reviews: Computational Molecular Science

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Simulation and design of energy materials accelerated by machine learning
Wiley Interdisciplinary Reviews: Computational Molecular Science ( IF 16.8 ) Pub Date : 2019-06-13 , DOI: 10.1002/wcms.1421
Hongshuai Wang ₁ , Yujin Ji ₁ , Youyong Li ₁

Affiliation

In the light of mature mathematical algorithms and material database construction, a basic research framework of machine learning (ML) method integrated with computational chemistry toolkits exhibits great potentials and advantages in the field of material researches. In this review, we introduce a work flow of ML in energy materials and demonstrate its recent applications in accelerating the material exploration, especially significant progresses in designing novel catalysts, organic and inorganic battery materials and metal–organic framework materials. As a rising research direction, we also identify the prospects and challenges of ML. More automated and intelligent workflows will be widely used in energy material design with the development of ML. Our review provides a guideline to study and design energy materials in the framework of ML.

中文翻译：

通过机器学习加速能源材料的仿真和设计

鉴于成熟的数学算法和材料数据库的构建，将机器学习（ML）方法与计算化学工具包集成在一起的基础研究框架在材料研究领域具有巨大的潜力和优势。在这篇综述中，我们介绍了ML在能源材料中的工作流程，并展示了ML在加速材料探索中的最新应用，特别是在设计新型催化剂，有机和无机电池材料以及金属有机框架材料方面的重大进展。作为上升的研究方向，我们还确定了机器学习的前景和挑战。随着ML的发展，更多的自动化和智能化工作流程将广泛用于能源材料设计中。我们的评论提供了在ML框架下研究和设计能源材料的指南。

更新日期：2019-11-18

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