Machine learning (ML) is shown to predict new alloys and their performances in a high dimensional, multiple-target-property design space that considers chemistry, multi-step processing routes, and characterization methodology variations. A physics-informed featured engineering approach is shown to enable otherwise poorly performing ML models to perform well with the same data. Specifically, previously engineered elemental features based on alloy chemistries are combined with newly engineered heat treatment process features. The new features result from first transforming the heat treatment parameter data as it was previously recorded using nonlinear mathematical relationships known to describe the thermodynamics and kinetics of phase transformations in alloys. The ability of the ML model to be used for predictive design is validated using blind predictions. Composition - process - property relationships for thermal hysteresis of shape memory alloys (SMAs) with complex microstructures created via multiple melting-homogenization-solutionization-precipitation processing stage variations are captured, in addition to the mean transformation temperatures of the SMAs. The quantitative models of hysteresis exhibited by such highly processed alloys demonstrate the ability for ML models to design for physical complexities that have challenged physics-based modeling approaches for decades.
Extracting Composition-Dependent Diffusion Coefficients Over a Very Large Composition Range in NiCoFeCrMn High Entropy Alloy Following Strategic Design of Diffusion Couples and Physics Informed Neural Network Numerical Method
Comprehensive Mapping of Tracer Diffusivities Across Composition Space in Ternary NiAlTi and Quinary NiCoFeAlTi High-Entropy Alloy Using Diffusion Couple Experiments and Physics Informed Neural Network Inversion
Machine learning based prediction of optical properties in two-dimensional Mo-W-S-Se-Te transition-metal dichalcogenide alloys through physics-informed sampling
Bangladesh University of Engineering and Technology（孟加拉国工程与技术大学）
Hydrogen permeability prediction in palladium alloys and virtual screening of B2-phase stabilized Pd(100-x-y)CuxMy ternary alloys using machine learning
AlloyVAE: A generative model for complex probabilistic field-to-field relationships in alloys
The Hong Kong University of Science and Technology（香港科技大学）；University of Toronto（多伦多大学）；Agency for Science, Technology and Research（新加坡科技研究局）；Johns Hopkins University（约翰斯·霍普金斯大学）；Tsinghua University（清华大学）；HKUST Shenzhen–Hong Kong Collaborative Innovation Research Institute（香港科技大学深圳-香港协同创新研究院）
MATAI: A Generalist Machine Learning Framework for Property Prediction and Inverse Design of Advanced Alloys
Nanyang Technological University（南洋理工大学）；Singapore Management University（新加坡管理大学）；CINTRA CNRS/NTU/THALES（CINTRA 法国国家科学研究中心/南洋理工大学/泰雷兹联合研究中心）；National University of Singapore（新加坡国立大学）
Chemo-mechanical coupling stabilizes mixed $\mathrm{Ag}_{x}\mathrm{Cu}_{1-x}\mathrm{GaSe}_{2}$ solar-cell absorbers: Insights from Monte-Carlo simulations assisted by ab initio informed machine-learning potentials
Institute of Materials Science, Technische Universitat Darmstadt（达姆施塔特工业大学材料科学研究所）
Physics-informed neural network for predicting fatigue life of unirradiated and irradiated austenitic and ferritic/martensitic steels under reactor-relevant conditions
Extreme Environments Materials Group (EEMG), Department of Materials Engineering, Indian Institute of Science (IISc)（极端环境材料组（EEMG）、材料工程系、印度科学研究院（IISc））；Department of Materials, University of Oxford（材料系、牛津大学）；Robert Bosch Centre for Cyberphysical Systems, Indian Institute of Science (IISc)（罗伯特·博世网络物理系统中心、印度科学研究院（IISc））
Physics-Informed Machine Learning for Steel Development: A Computational Framework and CCT Diagram Modelling
KTH Royal Institute of Technology（瑞典皇家理工学院）；Ferritico AB（Ferritico公司）；Freudenberg Group（科德宝集团）；Chongqing University（重庆大学）；Luleå University of Technology（吕勒奥理工大学）
Preference Learning from Physics-Based Feedback: Tuning Language Models to Design BCC/B2 Superalloys
Department of Computer Science, University of New Hampshire（新罕布什尔大学计算机科学系）；Materials Department, University of California, Santa Barbara（加州大学圣芭芭拉分校材料系）；Department of Mechanical Engineering, University of California, Santa Barbara（加州大学圣芭芭拉分校机械工程系）；Allen Institute for Artificial Intelligence（人工智能研究院）
