Wang Zhuo, Sun Zhehao, Yin Hang, Liu Xinghui, Wang Jinlan, Zhao Haitao, Pang Cheng Heng, Wu Tao, Li Shuzhou, Yin Zongyou, Yu Xue-Feng
Materials Interfaces Center, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, P. R. China.
Department of Chemical and Environmental Engineering, University of Nottingham Ningbo China, Ningbo, 315100, P. R. China.
Adv Mater. 2022 Sep;34(36):e2104113. doi: 10.1002/adma.202104113. Epub 2022 Jul 28.
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.
由于在提高实验和计算研究方法的准确性和效率方面的快速发展，所产生的大量数据已将材料科学领域带入了数据驱动型科学研究的第四范式。这种转变需要为材料创新的数据驱动方法开发权威且最新的框架。本文重点围绕框架、机器学习算法、特定材料数据库、描述符以及无机材料领域的目标应用，对数据驱动的材料发现的当前进展进行了批判性讨论。描述了使数据驱动的材料创新合理化的框架，并对基本子学科进行了批判性综述，包括：i）先进的数据密集型策略和机器学习算法；ii）材料数据库以及用于数据生成和管理的相关工具和平台；iii）数据驱动过程中常用的分子描述符。此外，还对材料创新的广泛应用进行了深入讨论，如能量转换与存储、环境净化、柔性电子、光电子、超导体、金属玻璃和磁性材料等。最后，概述了这些子学科（深入探讨材料科学、计算工具和数学的协同作用）如何支持数据驱动范式，并强调了数据驱动材料创新中的机遇与挑战。
