2.3k citations · 10.3k across the 79 of their papers we have counts for
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Invertible Coarse Graining with Physics-Informed Generative Artificial Intelligence
Jun Zhang, Xiaohan Lin, Weinan E +1
Multiscale molecular modeling is widely applied in scientific research of molecular properties over large time and length scales. Two specific challenges are commonly present in mu…
DeePN: A deep learning-based non-Newtonian hydrodynamic model
Lidong Fang, Pei Ge, Lei Zhang +2
A long standing problem in the modeling of non-Newtonian hydrodynamics of polymeric flows is the availability of reliable and interpretable hydrodynamic models that faithfully enco…
DeePKS: a comprehensive data-driven approach towards chemically accurate density functional theory
Yixiao Chen, Linfeng Zhang, Han Wang +1
We propose a general machine learning-based framework for building an accurate and widely-applicable energy functional within the framework of generalized Kohn-Sham density functio…
Integrating Machine Learning with Physics-Based Modeling
Weinan E, Jiequn Han, Linfeng Zhang
Machine learning is poised as a very powerful tool that can drastically improve our ability to carry out scientific research. However, many issues need to be addressed before this…
Deep Potential generation scheme and simulation protocol for the Li10GeP2S12-type superionic conductors
Jianxing Huang, Linfeng Zhang, Han Wang +3
It has been a challenge to accurately simulate Li-ion diffusion processes in battery materials at room temperature using {\it ab initio} molecular dynamics (AIMD) due to its high c…
Pushing the limit of molecular dynamics with ab initio accuracy to 100 million atoms with machine learning
Weile Jia, Han Wang, Mohan Chen +5
For 35 years, {\it ab initio} molecular dynamics (AIMD) has been the method of choice for modeling complex atomistic phenomena from first principles. However, most AIMD application…