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Wang Z, Sun Z, Yin H, Liu X, Wang J, Zhao H, Pang CH, Wu T, Li S, Yin Z, Yu XF. Data-Driven Materials Innovation and Applications. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2104113. [PMID: 35451528 DOI: 10.1002/adma.202104113] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Revised: 03/19/2022] [Indexed: 05/07/2023]
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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Affiliation(s)
- Zhuo Wang
- 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
| | - Zhehao Sun
- Research School of Chemistry, The Australian National University, ACT, 2601, Australia
| | - Hang Yin
- Research School of Chemistry, The Australian National University, ACT, 2601, Australia
| | - Xinghui Liu
- Department of Chemistry, Sungkyunkwan University (SKKU), 2066 Seoburo, Jangan-Gu, Suwon, 16419, Republic of Korea
| | - Jinlan Wang
- School of Physics, Southeast University, Nanjing, 211189, P. R. China
| | - Haitao Zhao
- Materials Interfaces Center, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, P. R. China
| | - Cheng Heng Pang
- Department of Chemical and Environmental Engineering, University of Nottingham Ningbo China, Ningbo, 315100, P. R. China
- Municipal Key Laboratory of Clean Energy Conversion Technologies, University of Nottingham Ningbo China, Ningbo, 315100, P. R. China
| | - Tao Wu
- Key Laboratory for Carbonaceous Wastes Processing and Process Intensification Research of Zhejiang Province, University of Nottingham Ningbo China, Ningbo, 315100, P. R. China
- New Materials Institute, University of Nottingham, Ningbo, China, Ningbo, 315100, P. R. China
| | - Shuzhou Li
- School of Materials Science and Engineering, Nanyang Technological University, Singapore, 639798, Singapore
| | - Zongyou Yin
- Research School of Chemistry, The Australian National University, ACT, 2601, Australia
| | - Xue-Feng Yu
- Materials Interfaces Center, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, P. R. China
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Predicting the onset temperature (T g) of Ge xSe 1-x glass transition: a feature selection based two-stage support vector regression method. Sci Bull (Beijing) 2019; 64:1195-1203. [PMID: 36659690 DOI: 10.1016/j.scib.2019.06.026] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2019] [Revised: 06/18/2019] [Accepted: 06/26/2019] [Indexed: 01/21/2023]
Abstract
Despite the usage of both experimental and topological methods, realizing a rapid and accurate measurement of the onset temperature (Tg) of GexSe1-x glass transition remains an open challenge. In this paper, a predictive model for the Tg in GexSe1-x glass system is presented by a machine learning method named feature selection based two-stage support vector regression (FSTS-SVR). Firstly, Pearson correlation coefficient (PCC) is used to select features highly correlated with Tg from the candidate features of GexSe1-x glass system. Secondly, in order to simulate the two-stage characteristic of Tg which is caused by structural variation with a turning point at x = 0.33 via the structural analysis, SVR is utilized to build predictive models for two stages separately and then the two achieved models are synthesized using a minimum error based model for Tg prediction. Compared with the topological and other methods based on SVR, the FSTS-SVR gives the highest predictive accuracy with the root mean square error (RMSE) and mean absolute percentage error (MAPE) of 10.64 K and 2.38%, respectively. This method is also expected to be more efficient for the prediction of Tg of other glass systems with the multi-stage characteristic.
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