Catalyze Materials Science with Machine Learning,ACS Materials Letters

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Catalyze Materials Science with Machine Learning
ACS Materials Letters ( IF 9.6 ) Pub Date : 2021-07-02 , DOI: 10.1021/acsmaterialslett.1c00204
Jaehyun Kim ₁ , Donghoon Kang ₁ , Sangbum Kim ₁ , Ho Won Jang _{1,

2}

Affiliation

Discovering and understanding new materials with desired properties are at the heart of materials science research, and machine learning (ML) has recently offered special shortcuts to the ultimate goal. Thanks to the nourishment of computer hardware and computational chemistry, the development of calculated scientific data repositories could fuel the ML models to investigate the vast materials space. At this moment, understanding this revolutionary paradigm is urgent, and this Review aims to deliver comprehensive information about the collaboration of ML with materials science. This Review summarizes recent achievements in catalysts design, which can be benefitted from ML because of the complex nature of catalytic reactions and vast candidate materials space. ML models for catalyst design could be transferred to applications in other domains and vice versa. The basic concepts of ML algorithms and practical guides to materials scientists are also demonstrated. Moreover, challenges and strategies of applying ML are discussed, which should be addressed collaboratively between materials scientists and ML communities. Ultimate integrations of ML in materials science are expected to accelerate the design, discovery, optimization, and interpretation of materials in both industry and academia, and this Review hopes to be the informative base camp for that journey.

中文翻译：

用机器学习催化材料科学

发现和理解具有所需特性的新材料是材料科学研究的核心，而机器学习 (ML) 最近为最终目标提供了特殊的捷径。由于计算机硬件和计算化学的发展，计算科学数据存储库的发展可以推动 ML 模型研究广阔的材料空间。此时此刻，了解这种革命性的范式迫在眉睫，本评论旨在提供有关 ML 与材料科学合作的全面信息。本综述总结了催化剂设计的最新成果，由于催化反应的复杂性和广阔的候选材料空间，可以从 ML 中受益。用于催化剂设计的 ML 模型可以转移到其他领域的应用中，反之亦然。还演示了 ML 算法的基本概念和材料科学家的实用指南。此外，还讨论了应用 ML 的挑战和策略，应在材料科学家和 ML 社区之间合作解决。ML 在材料科学中的最终整合有望加速工业界和学术界对材料的设计、发现、优化和解释，而本评论希望成为这一旅程的信息大本营。这应该在材料科学家和机器学习社区之间合作解决。ML 在材料科学中的最终整合有望加速工业界和学术界对材料的设计、发现、优化和解释，而本评论希望成为这一旅程的信息大本营。这应该在材料科学家和 ML 社区之间合作解决。ML 在材料科学中的最终整合有望加速工业界和学术界对材料的设计、发现、优化和解释，而本评论希望成为这一旅程的信息大本营。

更新日期：2021-08-02

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