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2201.01446
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Extending the limit of molecular dynamics with ab initio accuracy to 10 billion atoms
5 January 2022
Zhuoqiang Guo
Denghui Lu
Yujin Yan
Siyu Hu
Rongrong Liu
Guangming Tan
Ninghui Sun
Wanrun Jiang
Lijun Liu
Yixiao Chen
Linfeng Zhang
Mohan Chen
Han Wang
Weile Jia
AI4CE
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Papers citing
"Extending the limit of molecular dynamics with ab initio accuracy to 10 billion atoms"
6 / 6 papers shown
Title
DistMLIP: A Distributed Inference Platform for Machine Learning Interatomic Potentials
K. Han
Bowen Deng
Amir Barati Farimani
Gerbrand Ceder
124
3
0
28 May 2025
Scaling Molecular Dynamics with ab initio Accuracy to 149 Nanoseconds per Day
Jianxiong Li
Boyang Li
Zhuoqiang Guo
Mingzhen Li
Enji Li
Lijun Liu
Guojun Yuan
Zhan Wang
Guangming Tan
Weile Jia
AI4CE
123
4
0
30 Oct 2024
Overcoming systematic softening in universal machine learning interatomic potentials by fine-tuning
Bowen Deng
Yunyeong Choi
Peichen Zhong
Janosh Riebesell
Shashwat Anand
Zhuohan Li
KyuJung Jun
Kristin A. Persson
Gerbrand Ceder
AI4CE
133
21
0
11 May 2024
TensorMD: Scalable Tensor-Diagram based Machine Learning Interatomic Potential on Heterogeneous Many-Core Processors
Xin Chen
Yucheng Ouyang
Xin Chen
Zhenchuan Chen
Rongfen Lin
...
Lifang Wang
Fang Li
Yin Liu
Honghui Shang
Haifeng Song
AI4CE
59
2
0
12 Oct 2023
The Open MatSci ML Toolkit: A Flexible Framework for Machine Learning in Materials Science
Santiago Miret
Kin Long Kelvin Lee
Carmelo Gonzales
Marcel Nassar
Matthew Spellings
109
19
0
31 Oct 2022
Learning Local Equivariant Representations for Large-Scale Atomistic Dynamics
Albert Musaelian
Simon L. Batzner
A. Johansson
Lixin Sun
Cameron J. Owen
M. Kornbluth
Boris Kozinsky
209
534
0
11 Apr 2022
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