Applied Machine Learning for Developing Next-Generation Functional Materials

Dinic Filip; Singh Kamalpreet; Dong TonyRezazadeh MiladWang ZhiboKhosrozadeh AliYuan TiangeVoznyy Oleksandr

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Applied Machine Learning for Developing Next-Generation Functional Materials

机译：Applied Machine Learning for Developing Next-Generation Functional Materials

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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.

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来源
《Advanced functional materials》 |2021年第51期|2104195.1-2104195.18|共18页
作者
Dinic Filip; Singh Kamalpreet; Dong TonyRezazadeh MiladWang ZhiboKhosrozadeh AliYuan TiangeVoznyy Oleksandr;
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Univ Toronto, Dept Chem, Dept Phys & Environm Sci, Toronto, ON M1C 1A4, Canada;

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原文格式 PDF
正文语种英语
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batteries; electrocatalysis; machine learning; materials discovery; optoelectronics; solid-state electrolytes;

Applied Machine Learning for Developing Next-Generation Functional Materials

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