Applied Machine Learning for Developing Next-Generation Functional Materials,Advanced Functional Materials

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Applied Machine Learning for Developing Next-Generation Functional Materials
Advanced Functional Materials ( IF 18.5 ) Pub Date : 2021-09-13 , DOI: 10.1002/adfm.202104195
Filip Dinic ₁ , Kamalpreet Singh ₁ , Tony Dong ₁ , Milad Rezazadeh ₁ , Zhibo Wang ₁ , Ali Khosrozadeh ₁ , Tiange Yuan ₁ , Oleksandr Voznyy ₁

Affiliation

Machine learning (ML) is a versatile technique to rapidly and efficiently generate insights from multidimensional data. It offers a much-needed avenue to accelerate the exploration and investigation of new materials to address time-sensitive global challenges such as climate change. The availability of large datasets in recent years has enabled the development of ML algorithms for various applications including experimental/device optimization and material discovery. This perspective provides a summary of the recent applications of ML in material discovery in a range of fields, from optoelectronics to batteries and electrocatalysis, as well as an overview of the methods behind these advances. The paper also attempts to summarize some key challenges and trends in current research methodologies.

中文翻译：

用于开发下一代功能材料的应用机器学习

机器学习 (ML) 是一种通用技术，可以从多维数据中快速有效地生成见解。它提供了一条急需的途径来加速新材料的探索和研究，以应对气候变化等时间敏感的全球挑战。近年来，大型数据集的可用性使得 ML 算法能够开发用于各种应用，包括实验/设备优化和材料发现。这个观点总结了 ML 在一系列领域的材料发现中的最新应用，从光电子到电池和电催化，以及这些进步背后的方法的概述。本文还试图总结当前研究方法中的一些关键挑战和趋势。

更新日期：2021-09-13

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