Machine Learning for Materials Research and Development

doi:10.11900/0412.1961.2021.00357

Acta Metall Sin

2021, Vol. 57

Issue (11): 1343-1361 DOI: 10.11900/0412.1961.2021.00357

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Machine Learning for Materials Research and Development

XIE Jianxin¹(

), SU Yanjing¹(

), XUE Dezhen², JIANG Xue¹, FU Huadong¹, HUANG Haiyou¹

^1.Beijing Advanced Innovation Center for Materials Genome Engineering, Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing 100083, China
^2.State Key Laboratory for Mechanical Behavior of Materials, Xi'an Jiaotong University, Xi'an 710049, China

Cite this article:

XIE Jianxin, SU Yanjing, XUE Dezhen, JIANG Xue, FU Huadong, HUANG Haiyou. Machine Learning for Materials Research and Development. Acta Metall Sin, 2021, 57(11): 1343-1361.

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Abstract

The rapid advancement of big data and artificial intelligence has resulted in new data-driven materials research and development (R&D), which has achieved substantial progress. This fourth paradigm is believed to improve materials design efficiency and industrialized application and stimulate the discovery of new materials. The focus of this work is on the emerging field of machine learning-assisted material R&D, with an emphasis on machine learning predictions and optimization design. Following a brief description of feature construction and selection, recent developments in material predictions on phases/structures, processing-structure-property relationships, microstructure, and material performance are reviewed. This paper also summarizes the research progress on optimization algorithms with machine learning models, which is expected to overcome the bottlenecks such as the small size and high noise level of material data samples and huge space for exploration. The challenges and future opportunities for machine learning applications in materials R&D are discussed and prospected.

Key words: materials data data mining machine learning material design material genome engineering

Received: 25 August 2021

ZTFLH:

TP181

About author: SU Yanjing, professor, Tel: (010)62333884, E-mail: yjsu@ustb.edu.cn
XIE Jianxin, professor, Tel: (010)62332254, E-mail: jxxie@mater.ustb.edu.cn

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	Jianxin XIE
	Yanjing SU
	Dezhen XUE
	Xue JIANG
	Huadong FU
	Haiyou HUANG

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https://www.ams.org.cn/EN/10.11900/0412.1961.2021.00357 OR https://www.ams.org.cn/EN/Y2021/V57/I11/1343

Fig.1 Schematic workflow of phase prediction via high-throughput experiments and machine learning of Ni-base superalloys^[19] (TCP—topologically close-packed, GCP—geometric close-packed)

Fig.2 Flow map for the design of microstructure and mechanical property of steels with artificial neural network (ANN) (PF—polygonal ferrite; AF—acicular ferrite; GB—granular bainite; BF—bainitic ferrite; M—martensite; BH, DP, CP, and TRIP represent bake-hardening steel, dual phase steel, complex phase steel, and transformation induced plasticity steel; HSLA—high strength low alloys; L.R.Y.S represents linear regression of yield strength)^[45]

Fig.3 Property optimization workflow of TRIP Ti alloys based on artificial neural network (UTS—ultimate tensile strength, El—elongation, HT—heat treatment) (a, b)^[60]

Fig.4 Identification of tree structure (a) and regression models (b) via interpretable machine learning (X_i—the material parameter, GGG—gadolinium gallium garnet (Gd₃Ga₅O₁₂) crystals, S_STE—the spin-driven thermopower)^[78]

Fig.5 The Solid solution strengthening prediction via different physical models (MRE—the mean relative error, R—correlation coefficient, DFT—density functional theory)^[80]

Fig.6 Strategy of combining machine learning and genetic algorithm to design ultrahigh-strength stainless steel (PM—physical metallurgy, MAE—mean absolute error, SVM—support vector machine, SVC—support vector classifier, GA—genetic algorithm, VF—equilibrium volume fraction, DF—driving force)^[116]

Fig.7 Sequential filter strategy for multi-objective optimization of Co-base superalloys^[117]

Fig.8 Design strategy of precipitation strengthened copper alloys based on alloy factor screening and Bayesian optimization (MOEI represents the simultaneous improvement of mechanical and electrical properties based on the benchmark property μ^*, since EI_HV and EI_EC, respectively represent the property improvement (hardness, electrical conductivity) based on the benchmark properties $μ H V *$ and $μ E C *$ )^[123]

Fig.9 Transformation of multi-objective into single-objective optimization methods (θ^p—the angle between the target vector (ω^t) and the Pareto front vector (ω^p), δ^j—the distance from a point in the virtual space to the target)^[124]

Fig.10 Pareto front optimization diagram

Fig.11 Multi-objective Bayesian optimization framework (SMA—shape memory alloy, EHVI—expected hyper-volume improvement)^[128]

Fig.12 The materials reverse design process by encoding and decoding (SMILES—simplified molecular-input line-entry system)^[135]

Fig.13 Flowchart of the machine learning design system (MLDS) (a) and the performance of Cu alloys (b) (P2C—property to composition, C2P—composition to property)^[140]

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