2.3k citations · 9.1k across the 40 of their papers we have counts for
41 papers
Universal Machine-learning Molecular Dynamics at the Speed of Empirical Potentials
Tiancheng Li, Jianming Xue, Linfeng Zhang +2
No interatomic potential has offered universality across chemistry, near-first-principles accuracy and the speed of empirical potentials at once. Here we introduce DPA4C, an equiva…
Mixture of experts architectures for machine learning interatomic potentials
Yuzhi Liu, Duo Zhang, Anyang Peng +3
Machine Learning Interatomic Potentials (MLIPs) enable accurate large-scale atomistic simulations, yet improving their expressive capacity efficiently remains challenging. Here we…
A Graph Neural Network for the Era of Large Atomistic Models
Duo Zhang, Anyang Peng, Chun Cai +11
Foundation models, or large atomistic models (LAMs), aim to universally represent the ground-state potential energy surface (PES) of atomistic systems as defined by density functio…
LAMBench: A Benchmark for Large Atomistic Models
Anyang Peng, Chun Cai, Mingyu Guo +9
Large Atomistic Models (LAMs) have undergone remarkable progress recently, emerging as universal or fundamental representations of the potential energy surface defined by the first…
The OpenLAM Challenges
Anyang Peng, Xinzijian Liu, Ming-Yu Guo +2
Inspired by the success of Large Language Models (LLMs), the development of Large Atom Models (LAMs) has gained significant momentum in scientific computation. Since 2022, the Deep…
Deep variational free energy prediction of dense hydrogen solid at 1200K
Xinyang Dong, Hao Xie, Yixiao Chen +4
We perform deep variational free energy calculations to investigate the dense hydrogen system at 1200 K and high pressures. In this computational framework, neural networks are use…
Dflow, a Python framework for constructing cloud-native AI-for-Science workflows
Xinzijian Liu, Yanbo Han, Zhuoyuan Li +15
In the AI-for-science era, scientific computing scenarios such as concurrent learning and high-throughput computing demand a new generation of infrastructure that supports scalable…
An Extendable Cloud-Native Alloy Property Explorer
Zhuoyuan Li, Tongqi Wen, Yuzhi Zhang +8
The ability to rapidly evaluate materials properties through atomistic simulation approaches is the foundation of many new artificial intelligence-based approaches to materials ide…
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…
Machine-Learning-Based Interatomic Potentials for Group IIB to VIA Semiconductors: Towards a Universal Model
Jianchuan Liu, Xingchen Zhang, Tao Chen +4
Rapid advancements in machine-learning methods have led to the emergence of machine-learning-based interatomic potentials as a new cutting-edge tool for simulating large systems wi…
Imperfectly coordinated water molecules pave the way for homogeneous ice nucleation
Mingyi Chen, Lin Tan, Han Wang +2
Water freezing is ubiquitous on Earth, affecting many areas from biology to climate science and aviation technology. Probing the atomic structure in the homogeneous ice nucleation…
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…
Atomistic modelling of all dislocations and twins in HCP and BCC Ti
Tongqi Wen, Anwen Liu, Rui Wang +5
Ti exhibits complex plastic deformation controlled by active dislocation and twinning systems. Understandings on dislocation cores and twin interfaces are currently not complete or…
Classical and Machine Learning Interatomic Potentials for BCC Vanadium
Rui Wang, Xiaoxiao Ma, Linfeng Zhang +4
BCC transition metals (TMs) exhibit complex temperature and strain-rate dependent plastic deformation behaviour controlled by individual crystal lattice defects. Classical empirica…
Deep Variational Free Energy Approach to Dense Hydrogen
Hao Xie, Zi-Hang Li, Han Wang +2
We developed a deep generative model-based variational free energy approach to the equations of state of dense hydrogen. We employ a normalizing flow network to model the proton Bo…
Modeling the High-Pressure Solid and Liquid Phases of Tin from Deep Potentials with ab initio Accuracy
Tao Chen, Fengbo Yuan, Jianchuan Liu +4
Constructing an accurate atomistic model for the high-pressure phases of tin (Sn) is challenging because properties of Sn are sensitive to pressures. We develop machine-learning-ba…
DPA-1: Pretraining of Attention-based Deep Potential Model for Molecular Simulation
Duo Zhang, Hangrui Bi, Fu-Zhi Dai +3
Machine learning assisted modeling of the inter-atomic potential energy surface (PES) is revolutionizing the field of molecular simulation. With the accumulation of high-quality el…
DeePKS+ABACUS as a Bridge between Expensive Quantum Mechanical Models and Machine Learning Potentials
Wenfei Li, Qi Ou, Yixiao Chen +9
Recently, the development of machine learning (ML) potentials has made it possible to perform large-scale and long-time molecular simulations with the accuracy of quantum mechanica…
Deep Potentials for Materials Science
Tongqi Wen, Linfeng Zhang, Han Wang +2
To fill the gap between accurate (and expensive) ab initio calculations and efficient atomistic simulations based on empirical interatomic potentials, a new class of descriptions o…
Extending the limit of molecular dynamics with ab initio accuracy to 10 billion atoms
Zhuoqiang Guo, Denghui Lu, Yujin Yan +11
High-performance computing, together with a neural network model trained from data generated with first-principles methods, has greatly boosted applications of \textit{ab initio} m…
A deep potential model with long-range electrostatic interactions
Linfeng Zhang, Han Wang, Maria Carolina Muniz +3
Machine learning models for the potential energy of multi-atomic systems, such as the deep potential (DP) model, make possible molecular simulations with the accuracy of quantum me…
A Tungsten Deep Neural-Network Potential for Simulating Mechanical Property Degradation Under Fusion Service Environment
XiaoYang Wang, YiNan Wang, LinFeng Zhang +2
Tungsten is a promising candidate material in fusion energy facilities. Molecular dynamics (MD) simulations reveal the atomistic scale mechanisms, so they are crucial for the under…
DP Compress: a Model Compression Scheme for Generating Efficient Deep Potential Models
Denghui Lu, Wanrun Jiang, Yixiao Chen +4
Machine-learning-based interatomic potential energy surface (PES) models are revolutionizing the field of molecular modeling. However, although much faster than electronic structur…
DeePKS-kit: a package for developing machine learning-based chemically accurate energy and density functional models
Yixiao Chen, Linfeng Zhang, Han Wang +1
We introduce DeePKS-kit, an open-source software package for developing machine learning based energy and density functional models. DeePKS-kit is interfaced with PyTorch, an open-…