3 papers
physics.chem-ph2026
Mixture of experts architectures for machine learning interatomic potentials
Yuzhi Liu, Duo Zhang, Anyang Peng +3
Machine Learning Interatomic Potentials (MLIPs) enable accurate large-scale atomistic simulations, yet improving their expressive capacity efficiently remains challenging. Here we…
cond-mat.mtrl-sci2025
Integrating Deep-Learning-Based Magnetic Model and Non-Collinear Spin-Constrained Method: Methodology, Implementation and Application
Daye Zheng, Xingliang Peng, Yike Huang +8
We propose a non-collinear spin-constrained method that generates training data for deep-learning-based magnetic model, which provides a powerful tool for studying complex magnetic…
cond-mat.mtrl-sci2023
Machine-Learning-Based Interatomic Potentials for Group IIB to VIA Semiconductors: Towards a Universal Model
Jianchuan Liu, Xingchen Zhang, Tao Chen +4
Rapid advancements in machine-learning methods have led to the emergence of machine-learning-based interatomic potentials as a new cutting-edge tool for simulating large systems wi…