Machine learning in materials design: Algorithm and application*

可解释性 计算机科学 概化理论 大数据 领域(数学) 算法 人工智能 机器学习 反向 财产(哲学) 维数之咒 数据挖掘 数学 哲学 统计 认识论 纯数学 几何学
作者
Zhilong Song,Xiwen Chen,Fanbin Meng,Guanjian Cheng,Chen Wang,Zhongti Sun,Wan‐Jian Yin
出处
期刊:Chinese Physics B [IOP Publishing]
卷期号:29 (11): 116103-116103 被引量:37
标识
DOI:10.1088/1674-1056/abc0e3
摘要

Traditional materials discovery is in ‘trial-and-error’ mode, leading to the issues of low-efficiency, high-cost, and unsustainability in materials design. Meanwhile, numerous experimental and computational trials accumulate enormous quantities of data with multi-dimensionality and complexity, which might bury critical ‘structure–properties’ rules yet unfortunately not well explored. Machine learning (ML), as a burgeoning approach in materials science, may dig out the hidden structure–properties relationship from materials bigdata, therefore, has recently garnered much attention in materials science. In this review, we try to shortly summarize recent research progress in this field, following the ML paradigm: (i) data acquisition → (ii) feature engineering → (iii) algorithm → (iv) ML model → (v) model evaluation → (vi) application. In section of application, we summarize recent work by following the ‘material science tetrahedron’: (i) structure and composition → (ii) property → (iii) synthesis → (iv) characterization, in order to reveal the quantitative structure–property relationship and provide inverse design countermeasures. In addition, the concurrent challenges encompassing data quality and quantity, model interpretability and generalizability, have also been discussed. This review intends to provide a preliminary overview of ML from basic algorithms to applications.

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