领域(数学)
纳米技术
数据科学
材料信息学
数据驱动
计算机科学
数码产品
数据管理
系统工程
材料科学
人工智能
数据挖掘
工程类
医学
电气工程
公共卫生
健康信息学
工程信息学
护理部
纯数学
数学
作者
Zhuo Wang,Zhehao Sun,Hang Yin,Xinghui Liu,Jinlan Wang,Haitao Zhao,Cheng Heng Pang,Tao Wu,Shuzhou Li,Zongyou Yin,Xue‐Feng Yu
标识
DOI:10.1002/adma.202104113
摘要
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.
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