1 citations · 1 across the 2 of their papers we have counts for
3 papers
Does System Size Matter in Uncertainty Quantification for Machine Learning Interatomic Potentials?
Chayaphol Lortaraprasert, Nontawat Charoenphakdee, Wenwen Li +2
Machine-learning interatomic potentials (MLIPs) commonly use uncertainty estimates to flag out-of-distribution (OOD) configurations and select structures for active learning. A key…
LightPFP: A Lightweight Route to Ab Initio Accuracy at Scale
Wenwen Li, Nontawat Charoenphakdee, Yong-Bin Zhuang +6
Machine learning interatomic potentials (MLIPs) bridge the gap between quantum-mechanical accuracy and scalable molecular simulations. However, a fundamental trade-off persists: un…
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…