Mingjian Wen

Atomistic machine learning with irreducible Cartesian natural tensors

Abstract Atomistic machine learning is a powerful tool for accurate and efficient investigation of material behavior at the atomic scale. While attempts have been made to construct models directly within Cartesian space, they face challenges in providing a systematic framework ba...

Comparative study of ensemble-based uncertainty quantification methods for neural network interatomic potentials

Abstract Machine learning interatomic potentials (MLIPs) enable atomistic simulations with near first-principles accuracy at substantially reduced computational cost, making them powerful tools for large-scale materials modeling. The accuracy of MLIPs is typically validated on a...