Applied Machine Learning for Developing Next‐Generation Functional Materials. (13th September 2021)

• Record Type: Journal Article
• Authors: Dinic, Filip; Singh, Kamalpreet; Dong, Tony; Rezazadeh, Milad; Wang, Zhibo; Khosrozadeh, Ali; Yuan, Tiange; Voznyy, Oleksandr
• Is Part Of: Advanced functional materials. Volume 31:Number 51(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-09-13
• Subjects: batteries -- electrocatalysis -- machine learning -- materials discovery -- optoelectronics -- solid‐state electrolytes; Materials -- Periodicals; Chemical vapor deposition -- Periodicals; 620.11
• DOI: 10.1002/adfm.202104195
• Languages: English
• ISSNs: 1616-301X

Abstract

Abstract: 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. Abstract : This perspective provides a summary of the recent applications of machine learning 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 summarizes some key challenges and trends in current research methodologies.