MATAI: A Generalist Machine Learning Framework for Property Prediction and Inverse Design of Advanced Alloys

• URL: http://arxiv.org/abs/2511.10108v1
• Date: Fri, 14 Nov 2025 01:33:09 GMT
• Title: MATAI: A Generalist Machine Learning Framework for Property Prediction and Inverse Design of Advanced Alloys
• Authors: Yanchen Deng, Chendong Zhao, Yixuan Li, Bijun Tang, Xinrun Wang, Zhonghan Zhang, Yuhao Lu, Penghui Yang, Jianguo Huang, Yushan Xiao, Cuntai Guan, Zheng Liu, Bo An,
• Abstract summary: MATAI is a machine learning framework for inverse design of as-cast alloys.<n>It integrates a curated database, deep neural network-based property predictors, a constraint-aware optimization engine, and an iterative AI-experiment feedback loop.<n>We demonstrate MATAI's capabilities on the Ti-based alloy system, a canonical class of lightweight structural materials.
• Score: 42.606804375198465
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The discovery of advanced metallic alloys is hindered by vast composition spaces, competing property objectives, and real-world constraints on manufacturability. Here we introduce MATAI, a generalist machine learning framework for property prediction and inverse design of as-cast alloys. MATAI integrates a curated alloy database, deep neural network-based property predictors, a constraint-aware optimization engine, and an iterative AI-experiment feedback loop. The framework estimates key mechanical propertie, sincluding density, yield strength, ultimate tensile strength, and elongation, directly from composition, using multi-task learning and physics-informed inductive biases. Alloy design is framed as a constrained optimization problem and solved using a bi-level approach that combines local search with symbolic constraint programming. We demonstrate MATAI's capabilities on the Ti-based alloy system, a canonical class of lightweight structural materials, where it rapidly identifies candidates that simultaneously achieve lower density (<4.45 g/cm3), higher strength (>1000 MPa) and appreciable ductility (>5%) through only seven iterations. Experimental validation confirms that MATAI-designed alloys outperform commercial references such as TC4, highlighting the framework's potential to accelerate the discovery of lightweight, high-performance materials under real-world design constraints.

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