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Meeting 2027 TMS Annual Meeting & Exhibition
Symposium AI/ML/Data Informatics for Materials Discovery: Bridging Experiment, Theory, and Modeling
Presentation Title Language-Based Modeling for Additive-Manufacturing Alloy Design: Learning, Reasoning, and Generative Exploration
Author(s) Bo Ni, S. Mohadeseh Taheri-Mousavi
On-Site Speaker (Planned) Bo Ni
Abstract Scope Alloy design for additive manufacturing (AM) requires the convergence of thermodynamic calculations, kinetic and mechanical models, processing histories, experimental records, and printability criteria into actionable design decisions. Here we present language-based modeling as a framework for representing, learning, and reasoning over alloy physics for AM. In an autoregressive formulation, alloy compositions, process descriptors, property targets, and AM-specific context are serialized as scientific sequences, enabling generative language models to handle variable alloy records, learn composition-processing-property relations, and perform prediction and inverse recommendation beyond fixed surrogate models. Beyond learning, language-based modeling enables adaptive multi-agent reasoning: specialized agents translate underspecified AM design goals into physical criteria, retrieve knowledge, call CALPHAD tools, interpret failures, and revise workflows. we will also discuss diffusion-style language models as complementary generative models for parallel candidate refinement, faster generation, multi-objective convergence, and multimodal fusion. Overall, language-based modeling offers an organizing layer for adaptive, connected, and generative AM alloy design.
Proceedings Inclusion? Planned:
Keywords Machine Learning, ICME, Additive Manufacturing

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