| Affiliation: | 1. Material Science and Engineering Program, University of Colorado, Boulder, CO, USA;2. Department of Mechanical Engineering, Wichita State University, 1845 Fairmount St., Wichita, KS 67270, USA;3. School of Chemical and Metallurgical Engineering, University of the Witwatersrand, Johannesburg, South Africa;4. Department of Material Science and Engineering, University of Virginia, USA;5. LinkedIn Corporation, Mountain View, CA, USA;6. Department of Metallurgy, University of Johannesburg, South Africa;1. School of Mechanical Engineering, Yanshan University, Qinhuangdao City, Hebei, PR China;2. Department of Mechanical Engineering, University of Manitoba, Winnipeg, MB, Canada;1. Department of Industrial Management, National Taiwan University of Science and, Technology, Taipei 108, Taiwan;2. Department of Industrial Management, Can Tho University, Can Tho City 900000, Viet Nam;1. Department of Mechanical and Industrial Engineering, University of Illinois at Chicago, Chicago, IL 60607, USA;2. School of Computer Science and Technology, Xi’an University of Posts and Telecommunications, Xi’an 710121, China;3. School of Business, Jilin University, Changchun 130012, China |
| Abstract: | The application of machine learning (ML) techniques to metal-based nanomaterials has contributed greatly to understanding the interaction of nanoparticles, properties prediction, and new materials discovery. However, the prediction accuracy and efficiency of distinctive ML algorithms differ with different metal-based nanomaterials problems. This, alongside the high dimensionality and nonlinearity of available datasets in metal-based nanomaterials problems, makes it imperative to review recent advances in the implementation of ML techniques for these kinds of problems. In addition to understanding the applicability of different ML algorithms to various kinds of metal-based nanomaterials problems, it is hoped that this work will help facilitate understanding and promote interest in this emerging and less explored area of materials informatics. The scope of this review covers the introduction of metal-based nanomaterials, several techniques used in generating datasets for training ML models, feature engineering techniques used in nanomaterials-machine learning applications, and commonly applied ML algorithms. Then, we present the recent advances in ML applications to metal-based nanomaterials, with emphasis on the procedure and efficiency of algorithms used for such applications. In the concluding section, we identify the most common and efficient algorithms for distinctive property predictions. The common problems encountered in ML applications for metal-based nanoinformatics were mentioned. Finally, we propose suitable solutions and future outlooks for various challenges in metal-based nanoinformatics research. |
