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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 An Automated Probabilistic Machine Learning Framework for Materials Modeling and Active Learning: Benchmarking Uncertainty Quantification Across Analytical, Computational, and Experimental Domains
Author(s) Shakti Prasad Padhy, Juan Florez-Coronel, Sushant Kumar Sinha, Sk Md Ahnaf Akif Alvi, Robert Robinson, Jatin Kumar Ralhan, Raj Kiran, Douglas Allaire, Raymundo Arróyave
On-Site Speaker (Planned) Shakti Prasad Padhy
Abstract Scope Self-driving laboratories demand active learning loops with reliable exploration-exploitation balance, yet the cold-start problem, where models must be trusted with sparse or low-fidelity data, requires honest uncertainty quantification (UQ). We present an automated probabilistic machine learning framework benchmarking UQ fidelity across quantile-based and Bayesian regressors, with conformalized variants via jackknife+. Models are evaluated on four analytical benchmark functions spanning one to fifteen dimensions, then applied to three materials datasets: CALPHAD-calculated thermal properties for FeCoNiCrV alloys, FEA-derived elastic properties of porous particulate composites, and experimental Kerr rotation and coercivity for FeCoNi thin films. This progression from synthetic to computational to experimental data assesses UQ fidelity under increasing real-world complexity. The framework is deployed in a pool-based multi-objective batch Bayesian optimization loop for FeCoNi bulk alloy synthesis, seeded from 1,208 literature-curated data points alongside 40 ball milling and spark plasma sintering experiments, with model selection governed by a strictly proper scoring rule.
Proceedings Inclusion? Planned:
Keywords Computational Materials Science & Engineering, Modeling and Simulation, ICME

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