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cs.LG2026
Lang2MLIP: End-to-End Language-to-Machine Learning Interatomic Potential Development with Autonomous Agentic Workflows
Wenwen Li, Yuki Orimo, Nontawat Charoenphakdee
Developing machine learning interatomic potentials (MLIPs) for complex materials systems remains challenging because it requires expertise in atomistic simulations, machine learnin…
cs.LG2025
P-DRUM: Post-hoc Descriptor-based Residual Uncertainty Modeling for Machine Learning Potentials
Shih-Peng Huang, Nontawat Charoenphakdee, Yuta Tsuboi +2
Ensemble method is considered the gold standard for uncertainty quantification (UQ) in machine learning interatomic potentials (MLIPs). However, their high computational cost can l…