2 citations · 3 across the 5 of their papers we have counts for
8 papers
Atomistic prediction of magnetic behaviour across chemistry, structure and temperature
Ruosong Shi, Chengbing Chen, Bicheng Liu +15
Controlling magnetic phases and properties through composition and external conditions is central to the development of magnetic materials. Atomistic foundation models offer a rout…
Learning Electron Density with Virtual Atoms for Transferable Atomistic Representations
Yuzhi Liu, Xinzijian Liu, Chengqian Zhang +3
Electron density is a fundamental quantity of electronic structure, yet most machine learning density models treat it solely as a reconstruction target and are developed separately…
Universal Machine-learning Molecular Dynamics at the Speed of Empirical Potentials
Tiancheng Li, Jianming Xue, Linfeng Zhang +2
No interatomic potential has offered universality across chemistry, near-first-principles accuracy and the speed of empirical potentials at once. Here we introduce DPA4C, an equiva…
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…
Multi-Task Fine-Tuning Enables Robust Out-of-Distribution Generalization in Atomistic Models
Chengqian Zhang, Duo Zhang, Anyang Peng +7
Accurate de novo molecular and materials design requires structure-property models that generalize beyond known regimes. Although pretrained atomistic models achieve strong in-dist…
Scalable Canonical and Isothermal-Isobaric Sampling of Coupled Spin-Lattice Systems with Machine-Learning Potentials
Zhengtao Huang, Yunfei Bai, Han Wang +1
Magnetic machine-learning potentials (MLPs) now reach near-first-principles accuracy on the spin-lattice potential energy surface, but the dynamics and sampling frameworks that con…
A Graph Neural Network for the Era of Large Atomistic Models
Duo Zhang, Anyang Peng, Chun Cai +11
Foundation models, or large atomistic models (LAMs), aim to universally represent the ground-state potential energy surface (PES) of atomistic systems as defined by density functio…
LAMBench: A Benchmark for Large Atomistic Models
Anyang Peng, Chun Cai, Mingyu Guo +9
Large Atomistic Models (LAMs) have undergone remarkable progress recently, emerging as universal or fundamental representations of the potential energy surface defined by the first…