2.3k citations · 10.3k across the 79 of their papers we have counts for
79 papers
DPA-2: a large atomic model as a multi-task learner
Duo Zhang, Xinzijian Liu, Xiangyu Zhang +40
The rapid advancements in artificial intelligence (AI) are catalyzing transformative changes in atomic modeling, simulation, and design. AI-driven potential energy models have demo…
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…
DeePMD-kit v2: A software package for Deep Potential models
Jinzhe Zeng, Duo Zhang, Denghui Lu +44
DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials (MLP) known as Deep Potential (DP) models. T…
The Random Feature Method for Time-dependent Problems
Jingrun Chen, Weinan E, Yixin Luo
We present a framework for solving time-dependent partial differential equations (PDEs) in the spirit of the random feature method. The numerical solution is constructed using a sp…
MAC: A unified framework boosting low resource automatic speech recognition
Zeping Min, Qian Ge, Zhong Li +1
We propose a unified framework for low resource automatic speech recognition tasks named meta audio concatenation (MAC). It is easy to implement and can be carried out in extremely…
Bridging Traditional and Machine Learning-based Algorithms for Solving PDEs: The Random Feature Method
Jingrun Chen, Xurong Chi, Weinan E +1
One of the oldest and most studied subject in scientific computing is algorithms for solving partial differential equations (PDEs). A long list of numerical methods have been propo…