2 citations · 3 across the 9 of their papers we have counts for
12 papers
TrimSO(2): Fast and Memory-Efficient Training of Equivariant Interatomic Potentials
Chen Wang, Yuanchang Zhou, Guangming Tan +2
Equivariant interatomic potentials enable accurate and efficient simulations of molecules and materials. Among them, models based on SO(2) convolutions reduce the cost of tensor pr…
LunaTP: Enabling Efficient and Scalable Tensor Product for Equivariant MLIPs
Yuanchang Zhou, Mingzhen Li, Chen Wang +3
Equivariant machine learning interatomic potentials (MLIPs) promise atomistic simulations with accuracy approaching that of quantum-mechanical calculations. However, the high compu…
JanusPipe: Efficient Pipeline Parallel Training for Machine Learning Interatomic Potentials
Hongyu Wang, Weijian Liu, Hongtao Xu +4
Discovering atom-level phenomena requires molecular dynamics (MD) simulations with ab initio accuracy. Machine learning interatomic potentials (MLIPs) enable stable, high-accuracy…
MatRIS: Toward Reliable and Efficient Pretrained Machine Learning Interatomic Potentials
Yuanchang Zhou, Siyu Hu, Xiangyu Zhang +3
Foundation MLIPs demonstrate broad applicability across diverse material systems and have emerged as a powerful and transformative paradigm in chemical and computational materials…
Exploring Landscapes for Better Minima along Valleys
Tong Zhao, Jiacheng Li, Yuanchang Zhou +2
Finding lower and better-generalizing minima is crucial for deep learning. However, most existing optimizers stop searching the parameter space once they reach a local minimum. Giv…
Deep Learning-Enabled Supercritical Flame Simulation at Detailed Chemistry and Real-Fluid Accuracy Towards Trillion-Cell Scale
Zhuoqiang Guo, Runze Mao, Lijun Liu +3
For decades, supercritical flame simulations incorporating detailed chemistry and real-fluid transport have been limited to millions of cells, constraining the resolved spatial and…
WISCA: A Lightweight Model Transition Method to Improve LLM Training via Weight Scaling
Jiacheng Li, Jianchao Tan, Zhidong Yang +11
Transformer architecture gradually dominates the LLM field. Recent advances in training optimization for Transformer-based large language models (LLMs) primarily focus on architect…
Large Scale Finite-Temperature Real-time Time Dependent Density Functional Theory Calculation with Hybrid Functional on ARM and GPU Systems
Rongrong Liu, Zhuoqiang Guo, Qiuchen Sha +6
Ultra-fast electronic phenomena originating from finite temperature, such as nonlinear optical excitation, can be simulated with high fidelity via real-time time dependent density…
FastCHGNet: Training one Universal Interatomic Potential to 1.5 Hours with 32 GPUs
Yuanchang Zhou, Siyu Hu, Chen Wang +3
Graph neural network universal interatomic potentials (GNN-UIPs) have demonstrated remarkable generalization and transfer capabilities in material discovery and property prediction…
ALKPU: an active learning method for the DeePMD model with Kalman filter
Haibo Li, Xingxing Wu, Liping Liu +4
Neural network force field models such as DeePMD have enabled highly efficient large-scale molecular dynamics simulations with ab initio accuracy. However, building such models hea…
RLEKF: An Optimizer for Deep Potential with Ab Initio Accuracy
Siyu Hu, Wentao Zhang, Qiuchen Sha +5
It is imperative to accelerate the training of neural network force field such as Deep Potential, which usually requires thousands of images based on first-principles calculation a…
Backward error analysis of the Lanczos bidiagonalization with reorthogonalization
Haibo Li, Guangming Tan, Tong Zhao
The -step Lanczos bidiagonalization reduces a matrix into a bidiagonal form while generates two orthonormal matri…