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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 Learning Across Theories: A Multi-Fidelity Machine-Learned Interatomic Potential for the Energetic Molecular Crystal LLM-105
Author(s) Daniela Dantas Quintana, Paulo Branicio
On-Site Speaker (Planned) Daniela Dantas Quintana
Abstract Scope Energetic molecular crystals such as 2,6-diamino-3,5-dinitropyrazine-1-oxide (LLM-105), a high-energy-density material relevant to aerospace and defense propellant applications, are often poorly served by existing force fields, motivating transferable machine-learned potentials covering their constituent CHNO chemistry. Most such potentials, however, are trained on a single level of quantum-chemical theory, limiting how much data can be reused. We address this with a multi-fidelity interatomic potential built on the Allegro E(3)-equivariant architecture, conditioned on a learnable tag identifying each dataset's level of theory so data from different theories can be combined in one model. Training data includes MPtrj (VASP/PBE), SPICE2 (ωB97M-D3), OMC25 (VASP/PBE-D3(BJ)), and Transition1x (ωB97X), with base models reaching per-atom energy MAE of 0.003–0.06 eV and force MAE of 0.02–0.06 eV/Å. Fine-tuning on a dedicated VASP/PBE dataset for LLM-105 achieves per-atom energy MAE of 0.06 eV and force MAE of 0.03 eV/Å, sufficient to support predictive molecular dynamics simulation.
Proceedings Inclusion? Planned:
Keywords Machine Learning, Computational Materials Science & Engineering, Modeling and Simulation

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