4 papers
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…
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…
LightPFP: A Lightweight Route to Ab Initio Accuracy at Scale
Wenwen Li, Nontawat Charoenphakdee, Yong-Bin Zhuang +5
Atomistic simulation methods have evolved through successive computational levels, each building upon more fundamental approaches: from quantum mechanics to density functional theo…
Dielectric Tensor Prediction for Inorganic Materials Using Latent Information from Preferred Potential
Zetian Mao, Wenwen Li, Jethro Tan
Dielectrics are crucial for technologies like flash memory, CPUs, photovoltaics, and capacitors, but public data on these materials are scarce, restricting research and development…