Data‐Driven Materials Innovation and Applications. Issue 36 (28th July 2022)

• Record Type: Journal Article
• Authors: Wang, Zhuo; Sun, Zhehao; Yin, Hang; Liu, Xinghui; Wang, Jinlan; Zhao, Haitao; Pang, Cheng Heng; Wu, Tao; Li, Shuzhou; Yin, Zongyou; Yu, Xue‐Feng
• Is Part Of: Advanced materials. Volume 34:Issue 36(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-07-28
• Subjects: data‐driven research -- machine learning -- material applications -- material informatics -- material innovation; Materials -- Periodicals; Chemical vapor deposition -- Periodicals; 620.11
• DOI: 10.1002/adma.202104113
• Languages: English
• ISSNs: 0935-9648

Abstract

Abstract: Owing to the rapid developments to improve the accuracy and efficiency of both experimental and computational investigative methodologies, the massive amounts of data generated have led the field of materials science into the fourth paradigm of data‐driven scientific research. This transition requires the development of authoritative and up‐to‐date frameworks for data‐driven approaches for material innovation. A critical discussion on the current advances in the data‐driven discovery of materials with a focus on frameworks, machine‐learning algorithms, material‐specific databases, descriptors, and targeted applications in the field of inorganic materials is presented. Frameworks for rationalizing data‐driven material innovation are described, and a critical review of essential subdisciplines is presented, including: i) advanced data‐intensive strategies and machine‐learning algorithms; ii) material databases and related tools and platforms for data generation and management; iii) commonly used molecular descriptors used in data‐driven processes. Furthermore, an in‐depth discussion on the broad applications of material innovation, such as energy conversion and storage, environmental decontamination, flexible electronics, optoelectronics, superconductors, metallic glasses, and magnetic materials, is provided. Finally, how these subdisciplines (with insights into the synergy of materials science, computational tools, and mathematics) support data‐driven paradigms is outlined, and the opportunities and challenges in data‐driven material innovation are highlighted. Abstract : The recent advances, strategies, insights, and challenges of data‐driven‐based innovations and applications in material science are discussed. Essential subdisciplines, including framework, machine‐learning algorithms, available chemical databases, commonly used key descriptors, and innovations and applications based on their synergy, are reviewed.