Machine learning in materials design: Algorithm and application

doi:10.1088/1674-1056/abc0e3

Abstract

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.

Keywords: machine learning materials design structure-property relationship active learning

Received: 06 July 2020
Revised: 24 August 2020
Accepted manuscript online: 14 October 2020

Fund: Project support by the National Natural Science Foundation of China (Grant Nos. 11674237 and 51602211), the National Key Research and Development Program of China (Grant No. 2016YFB0700700), the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), China, and China Post-doctoral Foundation (Grant No. 7131705619).

Corresponding Authors: ^†Corresponding author. E-mail: ztsun@suda.edu.cn ^‡Corresponding author. E-mail: wjyin@suda.edu.cn

Cite this article:

Zhilong Song(宋志龙), Xiwen Chen(陈曦雯), Fanbin Meng(孟繁斌), Guanjian Cheng(程观剑), Chen Wang(王陈), Zhongti Sun(孙中体), and Wan-Jian Yin(尹万健) Machine learning in materials design: Algorithm and application 2020 Chin. Phys. B 29 116103

Fig. 1.

The main workflow of traditional supervised learning and active learning.

Table 1.

HT tools.

Table 2.

Material databases.

Table 3.

Feature tools.

Fig. 2.

A simple fully connected neural network structure with two hidden layers.

Fig. 3.

A 2D CNN structure with two max-pooling layers and one convolution layer.

Fig. 4.

The sketch of the methodology of RNN.

Fig. 5.

The workflow of the SR program.

Fig. 6.

The structure of the AI Feynman. Reprinted with permission from Ref. [137].

Fig. 7.

The mechanism of the BO method. Reprinted with permission from Ref. [150]. Copyright (2020) Springer Nature.

Fig. 8.

Four main parts of MCTS.

Fig. 9.

Typical learning curve, y axis refers to the value of loss function, and x axis is the number of examples.

Fig. 10.

(a) The workflow of classification of XRD data with data augmentation method. (b) The structure of CNN model they used. Reprinted with permission from Ref. [2].

Table 4.

ML application for some materials properties.

Fig. 11.

The main workflow of screening stable halide perovskites via ML in combination with DFT calculations. Reprinted with permission from Ref. [192]. Copyright (2019) John Wiley and Sons.

Fig. 12.

(a) The search progress for the halide perovskites with ideal decomposition energy and band gap. (b) The performance of BO. Reprinted with permission from Ref. [150]. Copyright (2020) Springer Nature.

Fig. 13.

Workflow of screening semiconductors from the MXene database and predicting band gaps. Reprinted with permission from Ref. [200]. Copyright (2018) American Chemical Society.

Fig. 14.

(a) The representation matrix of doped graphene supercell systems. (b) One of the CNN structures to predict band gaps. Reprinted with permission from Ref. [20].

Fig. 15.

(a) Prediction on testing set of low-T_c, iron-based, and cuprate superconductors. (b)–(c) Prediction from model trained on data only containing low-T_c materials. (d)–(e) Prediction from model trained on data only containing cuprate materials. Reprinted with permission from Ref. [19].

Fig. 16.

(a) The prediction result for ITR from LSBoost. (b) The relation between ITR prediction and the thickness and temperature. Reprinted with permission from Ref. [226]. Copyright (2018) American Chemical Society.

Fig. 17.

The workflow of MCTS for optimizing the surface roughness. Reprinted with permission from Ref. [229].

Fig. 18.

(a) The design loop for searching high-temperature ferroelectric perovskites. Reprinted from Ref. [6]. (b) The relation between the T_c and chemical composition for the ternary system Al–Co–Fe. Reprinted with permission from Ref. [236]. Copyright (2019) American Physical Society.

Fig. 19.

The workflow of the work of Raccuglia^[78] et al. Copyright (2016) Springer Nature.

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