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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 User-Friendly CALPHAD Workflows for Materials Discovery Beyond the CALPHAD Community
Author(s) Moritz To Baben, Florian Tang, Bruno Reis, Cassie Früh, Philipp Keuter, Alexander Walnsch
On-Site Speaker (Planned) Moritz To Baben
Abstract Scope CALPHAD can translate atomistic and experimental data into thermodynamic models, but assessment has remained specialist-intensive. We present user-friendly, agentic CALPHAD workflows that combine Calphad Optimizer in FactSage [1], machine-learning-assisted thermochemical and temperature extrapolation [2], ThermML-based semantic provenance [3], and DFT-informed (defect) energetics integrated with experimental phase-equilibrium constraints [4]. For agentic use, skills are used for data ingestion, model setup, optimization and validation. This enables experimentalists, atomistic modelers, and AI agents to contribute heterogeneous data while preserving thermodynamic rigor. Rather than replacing expert assessment, the approach lowers the entry barrier, highlights data gaps, and builds auditable, reusable thermodynamic descriptions that connect experiment, theory, and modeling. [1] Reis et al., Calphad 88 (2025) 102800. [2] Frueh et al., Calphad 90 (2025) 102838. [3] https://github.com/flotang-gtt/ThermML. [4] A. Walnsch et al., Acta Materialia 316 (2026) 122469.
Proceedings Inclusion? Planned:
Keywords Modeling and Simulation, Machine Learning, Computational Materials Science & Engineering

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