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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 Trustworthy, Uncertainty-Aware Multimodal Machine Learning for Materials Discovery: Interpretable, Self-Curating Screening of Solid-State Electrolytes
Author(s) Qingjie Li, Joshua Young, Taku Watanabe
On-Site Speaker (Planned) Qingjie Li
Abstract Scope Reliable materials discovery increasingly demands AI that is not only accurate but trustworthy—able to quantify its confidence and improve its own training data. Using solid-state electrolyte discovery, I will present MuLiCon, a physics-informed, uncertainty-aware multimodal framework bridging molecular-dynamics (MD) modeling and experimental decision-making. MuLiCon fuses three physical views of each material: foundational chemistry from a universal machine-learning interatomic potential, structure from simulated X-ray diffraction, and dynamics from species-separated phonon density of states. A dual-task classifier–regressor with a deep ensemble yields aleatoric and epistemic uncertainty, enabling active abstention on ambiguous cases rather than overconfident false positives. Under cross-validation, abstention improves reliability; a retrained modality ablation reveals a division of labor: foundational chemistry forms the predictive backbone, while interpretable diffraction and phonon signals add complementary classification and precision gains. The same uncertainty drives a closed loop: uncertainty-guided screening of unsimulated databases alongside a self-curating strategy that re-simulates the least-reliable labels.
Proceedings Inclusion? Planned:
Keywords Energy Conversion and Storage, Machine Learning, Modeling and Simulation

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