Overview: Integration and Development of Physical Models and Artificial Intelligence in Alloy Design

doi:10.11900/0412.1961.2024.00355

Acta Metall Sin

2025, Vol. 61

Issue (4): 541-560 DOI: 10.11900/0412.1961.2024.00355

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Overview: Integration and Development of Physical Models and Artificial Intelligence in Alloy Design

WANG Chenchong, XU Wei(

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State Key Laboratory of Digital Steel, Northeastern University, Shenyang 110819, China

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WANG Chenchong, XU Wei. Overview: Integration and Development of Physical Models and Artificial Intelligence in Alloy Design. Acta Metall Sin, 2025, 61(4): 541-560.

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Abstract

With the rise of data-driven methods as the fourth scientific paradigm, their impact on the third paradigm—physical model-driven approaches—has been significant in the field of alloy design. However, neither paradigm can overcome the trade-off between model accuracy and interpretability, particularly in mechanical performance design. As a consequence, they fail to meet the efficiency and rationality requirements necessary for alloy development within Material Genome Engineering, especially for metal structural materials. This challenge has led to the emergence of the fifth paradigm, AI4Sci, in alloy design. This article provides an overview of various cases employing the physical metallurgy-guided artificial intelligence method system. It systematically explains how to integrate physical models and mechanisms with artificial intelligence at three levels: numerical data guidance, image data guidance, and mechanism guidance. This approach aims to resolve the inherent trade-off between accuracy and interpretability in alloy design. In addition, it explores the theoretical foundations, advantages, and limitations of three paradigms—multi-scale physical models, artificial intelligence, and AI4Sci—within the field. For cross-scale modeling and materials science large models, this article offers insights into conceptual frameworks and technical methodologies for the future development of each scientific paradigm in alloy design.

Key words: alloy design material genome engineering artificial intelligence cross scale modeling AI4Sci

Received: 22 October 2024

ZTFLH:

TG111.8

Fund: National Key Research and Development Program of China(2023YFB3712403);National Natural Science Foundation of China(U22A20106, 52311530082)

Corresponding Authors: XU Wei, professor, Tel: (024)83680246, E-mail: xuwei@ral.neu.edu.cn

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https://www.ams.org.cn/EN/10.11900/0412.1961.2024.00355 OR https://www.ams.org.cn/EN/Y2025/V61/I4/541

Fig.1 Essential nature of the mutually exclusive relationship between model interpretability and accuracy
(a) trade-off between model accuracy and interpretability (AI—artificial intelligence)
(b) high degrees of freedom in equations
(c) low degrees of freedom in equations
(d) high degrees of freedom in equations constrained by physics

Fig.2 Quantitative effect of dimensionality reduction methods on model accuracy^[29-32] (PCA—principal components analysis, KPCA—kernel based principal component analysis, t-SNE—t-distributed stochastic neighbor embedding, ISOMAP—Isometric Mapping)

Fig.3 Cross scale system of physical metallurgy-guided artificial intelligence (ML—machine learning, V_m—mole fraction, UTS—ultimate tensile strength, UE—uniform elongation, BC—band contrast, KAM—kernel average misorientation, CAM—Class Activation Mapping)

Fig.4 Comparisons of accuracy and extensibility of martensite transformation start temperature (M_s) computation models^[43] (MAE—mean absolute error, SVM—support vector machine, DDM-CNN—deep data mining guided convolutional neural network)

Fig.5 Paradigm of thermodynamic mechanism information-guided artificial intelligence for alloy design^[10,46-54] (ANN—artificial neural network)

Fig.6 Paradigm of multi-scale mechanism information-guided artificial intelligence for design^[60-67] (DFT—density functional theory, FEM—finite element model)

Fig.7 Comparisons of algorithms for guiding artificial intelligence with image core information^[73-75] (DL—deep learning, SAM—segment anything model, ML1 and ML2 represent different machine learning models for regression)
(a1-a12) large sample, low accuracy
(b1-b5) small sample, high accuracy
(c1-c16) no sample, medium accuracy

Fig.8 Various multimodal algorithm frameworks and their advantages and disadvantages

Fig.9 Mechanism correlation and data requirements of various image algorithms^{[73,76,87,90]}

Fig.10 Methods and applications of mechanism guided artificial intelligence (PINN—physics-informed neural network, NN—neural network, PDE—partial differential equation, MSE—mean squared error. x—spatial variable, t—temporal variable, w—weight, b—bias, σ—activation function, u—solution of the partial differential equation and also the output of the neural network, L—differential operator, g—known function on the right-hand side of the partial differential equation, θ—parameter set of the partial differential equation, R—residual value, ε—threshold. MSE_{_u_{, BC, IC}}—mean squared error of the solution u when considering boundary conditions (BC) and initial conditions (IC), MSER is the mean squared error based on the R)

Fig.11 Future development directions of the five scientific paradigms in the field of alloy design

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