As the big data generated by the development of modern experiments and computing technology becomes more and more accessible, the material design method based on machine learning (ML) has opened a new paradigm for materials science research. With its ability to automatically solve complex tasks, machine learning is being used as a new method to help discover the relevance of materials, understand materials' properties, and accelerate the discovery of materials. This paper first introduces the general process of machine learning in materials science. Secondly, the applications of machine learning in material properties prediction, classification and identification, auxiliary micro-scale characterization, phase transformation research and phase diagram construction, process optimization, service behavior evaluation, accelerating the development of computational simulation technology, multi-objective optimization and inverse design of materials are reviewed. Finally, we discuss the main challenges and possible solutions in machine learning, and predict the potential research directions. 

Machine learning accelerates the materials discovery

Multi-principal element alloys (MPEAs) with remarkable performances possess great potential as structural, functional, and smart materials. However, their efficient performance-orientated design in a wide range of compositions and types is an extremely challenging issue, because of properties strongly dependent upon the composition and composition-dominated microstructure. Here, we propose a multistage-design approach integrating machine learning, physical laws and a mathematical model for developing the desired-property MPEAs in a very time-efficient way. Compared to the existing physical model- or machine-learning-assisted material development, the forward-and-inverse problems, including identifying the target property and unearthing the optimal composition, can be tackled with better efficiency and higher accuracy using our proposed avenue, which defeats the one-step component-performance design strategy by multistage-design coupling constraints. Furthermore, we developed a new multi-phase MPEA at the minimal time and cost, whose high strength-ductility synergy exceeded those of its system and subsystem reported so far by searching for the optimal combination of phase fraction and composition. The present work suggests that the property-guided composition and microstructure are precisely tailored through the newly built approach with significant reductions of the development period and cost, which is readily extendable to other multi-principal element materials.

Performance-oriented multistage design for multi-principal element alloys with low cost yet high efficiency.

New insight in predicting martensite start temperature in steels

The phase transformation temperature plays an important role in the design, production and heat treatment process of steels. In the present work, an improved version of the gradient-boosting method LightGBM has been utilized to study the influencing factors of the four phase transformation temperatures, namely Ac1, Ac3, the martensite transformation start (MS) temperature and the bainitic transformation start (BS) temperature. The effects of the alloying element were discussed in detail by comparing their influencing mechanisms on different phase transformation temperatures. The training accuracy was significantly improved by further introducing appropriate features related to atomic parameters. The melting temperature and coefficient of linear thermal expansion of the pure metals corresponding to the alloying elements, atomic Waber–Cromer pseudopotential radii and valence electron number were the top four among the eighteen atomic parameters used to improve the trained model performance. The training and prediction processes were analyzed using a partial dependence plot (PDP) and Shapley additive explanation (SHAP) methods to reveal the relationships between the features and phase transformation temperature.

Phase Transformation Temperature Prediction in Steels via Machine Learning

Using a machine learning (ML) approach to fit DFT data, interatomic potentials have been successfully extracted. In this study, the phase transition, mechanical behavior and lattice thermal conductivity are investigated for halogen perovskites using NEP-based MD simulations in a large supercell including 16 000 atoms, which breaks through the size and temperature effects in DFT. A clear phase transition from orthorhombic (γ) → tetragonal (β) → cubic (α) is observed during the heating process. During the cooling process, CsPbCl3 and CsPbBr3 exhibit perfect reversible behavior, while CsPbI3 only undergoes a phase transition from α to β. Then, the key mechanical parameters, including Poisson's ratio, tensile strength, critical strain and bulk modulus, are predicted. The thermal conductivity is also investigated using the NEP-based MD simulations. At room temperature, they exhibit extremely low thermal conductivity. The predicted results are compared with the experimental results, and the rationality of ML potentials has been confirmed.

Investigation of phase transition, mechanical behavior and lattice thermal conductivity of halogen perovskites using machine learning interatomic potentials.

Machine learning assisted empirical formula augmentation

The invention provides ultra-clean earth steel and an occluded foreign substance modification control method. 10-200 ppm of rare earth elements are included in the steel, and 50% or more occluded foreign substances in the steel are dispersed RE-oxygen-sulfide with the average equivalent diameter Dmean ranging from 1 micrometer to 5 micrometers in a spherical shape or a spheroidal shape or a granular shape. According to the method, at least 80%, preferably at least 90%, of Al2O3 occluded foreign substances in the steel are modified into RE-oxygen-sulfide, compared with steel with the same components without rare earth, the total amount of the occluded foreign substances in the steel is reduced by 18% or higher, the cracking probability caused by occluded foreign substances such as Al2O3 intraditional pure steel is reduced, the mechanical and chemical performance such as the fatigue life of the steel is remarkably improved, precise control over the modification type, distribution and dimension of the occluded foreign substances in the steel is achieved, and the method is suitable for researching and producing of high-performance steel in more varieties.

Ultra-clean rare earth steel and occluded foreign substance modification control method

Inducing mechanism and model of the critical oxygen content in homogenized steel

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Machine learning assisted empirical formula augmentation

Ultra-clean rare earth steel and occluded foreign substance modification control method

Inducing mechanism and model of the critical oxygen content in homogenized steel