249 citations · 279 across the 40 of their papers we have counts for
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Beyond Adam: Disentangling Optimizer Effects in the Fine-Tuning of Atomistic Foundation Models
Xiaoqing Liu, Yangshuai Wang, Teng Zhao
Atomistic foundation models constitute a paradigm shift in computational materials science by providing universal machine-learned interatomic potentials with broad transferability…
MicroEvoEval: A Systematic Evaluation Framework for Image-Based Microstructure Evolution Prediction
Qinyi Zhang, Duanyu Feng, Ronghui Han +2
Simulating microstructure evolution (MicroEvo) is vital for materials design but demands high numerical accuracy, efficiency, and physical fidelity. Although recent studies on deep…
Higher-Order Boundary Conditions for Atomistic Dislocation Simulations
Xinyi Wei, Julian Braun, Yangshuai Wang +1
We present a higher-order boundary condition for atomistic simulations of dislocations that address the slow convergence of standard supercell methods. The method is based on a mul…
Flexible Uncertainty Calibration for Machine-Learned Interatomic Potentials
Cheuk Hin Ho, Christoph Ortner, Yangshuai Wang
Reliable uncertainty quantification (UQ) is essential for developing machine-learned interatomic potentials (MLIPs) in predictive atomistic simulations. Conformal prediction (CP) i…
A Conformal Prediction Framework for Uncertainty Quantification in Physics-Informed Neural Networks
Yifan Yu, Cheuk Hin Ho, Yangshuai Wang
Physics-Informed Neural Networks (PINNs) have emerged as a powerful framework for solving PDEs, yet existing uncertainty quantification (UQ) approaches for PINNs generally lack rig…
A Study on the Fine-Tuning Performance of Universal Machine-Learned Interatomic Potentials (U-MLIPs)
Xiaoqing Liu, Kehan Zeng, Yangshuai Wang +1
Universal machine-learned interatomic potentials (U-MLIPs) have demonstrated effectiveness across diverse atomistic systems but often require fine-tuning for task-specific accuracy…
Fine-Tuning Universal Machine-Learned Interatomic Potentials: A Tutorial on Methods and Applications
Xiaoqing Liu, Kehan Zeng, Zedong Luo +3
Universal machine-learned interatomic potentials (U-MLIPs) have demonstrated broad applicability across diverse atomistic systems but often require fine-tuning to achieve task-spec…
An Atomic Cluster Expansion Potential for Twisted Multilayer Graphene
Yangshuai Wang, Drake Clark, Sambit Das +5
Twisted multilayer graphene, characterized by its moiré patterns arising from inter-layer rotational misalignment, serves as a rich platform for exploring quantum phenomena. Machin…
Formulation and Analysis of Blended Atomistic to Higher-Order Continuum Coupling Methods for Crystalline Defects
Junfeng Lu, Hao Wang, Yangshuai Wang
Concurrent multiscale methods play an important role in modeling and simulating materials with defects, aiming to achieve the balance between accuracy and efficiency. Atomistic-to-…
A General Framework of Linear Elasticity Enhanced Multiscale Coupling Methods for Crystalline Defects
Yanbo Zhan, Yangshuai Wang, Hao Wang
The atomistic-to-continuum (a/c) coupling methods, also known as the quasicontinuum (QC) methods, are a important class of concurrent multisacle methods for modeling and simulating…
Many-Body Coarse-Grained Molecular Dynamics with the Atomic Cluster Expansion
Yangshuai Wang, Gabor Csanyi, Christoph Ortner
Molecular dynamics (MD) simulations provide detailed insight into atomic-scale mechanisms but are inherently restricted to small spatio-temporal scales. Coarse-grained molecular dy…