{"ID":2842169,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.10108","arxiv_id":"2511.10108","title":"MATAI: A Generalist Machine Learning Framework for Property Prediction and Inverse Design of Advanced Alloys","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 (\u003c4.45 g/cm3), higher strength (\u003e1000 MPa) and appreciable ductility (\u003e5%) 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.","short_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 allo...","url_abs":"https://arxiv.org/abs/2511.10108","url_pdf":"https://arxiv.org/pdf/2511.10108v1","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\"]","published":"2025-11-13T09:15:56Z","proceeding":"cond-mat.mtrl-sci","tasks":"[\"cond-mat.mtrl-sci\",\"cs.AI\"]","methods":"[]","has_code":false}