Application of Supervised Learning Algorithms in Materials Science

doi:10.11871/jfdc.issn.2096-742X.2023.04.004

Abstract

Abstract:

[Objective] This article aims to provide a brief overview of the applications of machine learning in materials research in recent years, offering a reference for related studies. [Literature Scope] Therefore, this article mainly references recent literature and materials databases and research papers utilizing machine learning algorithms for material property prediction and new material discovery. [Methods] The article introduces the workflow of supervised machine learning and presents the current applications of various supervised machine learning algorithms in materials science. [Results] Machine learning algorithms help to identify patterns between material properties and factors such as composition elements and crystal structures, making them significant in the discovery of new materials. Additionally, force field methods using machine learning demonstrate potential in addressing complex phenomena like phase transitions and interfaces. [Limitations] Due to the limitations of the author’s expertise, the focus of the article is primarily on the application of supervised machine learning methods in material property prediction and a few other areas. Citations regarding to unsupervised learning and other research fields in materials science are currently inadequate. [Conclusions] This is an emerging field that is expected to become an important component of materials science in the future.

Key words: machine learning, materials science, neural networks, algorithms, properties prediction

LIU Duanyang, WEI Zhongming. Application of Supervised Learning Algorithms in Materials Science[J]. Frontiers of Data and Computing, 2023, 5(4): 38-47, https://cstr.cn/32002.14.jfdc.CN10-1649/TP.2023.04.004.

Figures/Tables 4

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