596 citations · 986 across the 10 of their papers we have counts for
11 papers
Safe-SDL:Establishing Safety Boundaries and Control Mechanisms for AI-Driven Self-Driving Laboratories
Zihan Zhang, Haohui Que, Junhan Chang +3
The emergence of Self-Driving Laboratories (SDLs) transforms scientific discovery methodology by integrating AI with robotic automation to create closed-loop experimental systems c…
UniLabOS: An AI-Native Operating System for Autonomous Laboratories
Jing Gao, Junhan Chang, Haohui Que +12
Autonomous laboratories promise to accelerate discovery by coupling learning algorithms with robotic experimentation, yet adoption remains limited by fragmented software that separ…
Bohrium + SciMaster: Building the Infrastructure and Ecosystem for Agentic Science at Scale
Linfeng Zhang, Siheng Chen, Yuzhu Cai +46
AI agents are emerging as a practical way to run multi-step scientific workflows that interleave reasoning with tool use and verification, pointing to a shift from isolated AI-assi…
DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials
Jinzhe Zeng, Duo Zhang, Anyang Peng +44
In recent years, machine learning potentials (MLPs) have become indispensable tools in physics, chemistry, and materials science, driving the development of software packages for m…
Efficient and Precise Force Field Optimization for Biomolecules Using DPA-2
Junhan Chang, Duo Zhang, Yuqing Deng +5
Molecular simulations are essential tools in computational chemistry, enabling the prediction and understanding of molecular interactions and thermodynamic properties of biomolecul…
Uni-SMART: Universal Science Multimodal Analysis and Research Transformer
Hengxing Cai, Xiaochen Cai, Shuwen Yang +14
In scientific research and its application, scientific literature analysis is crucial as it allows researchers to build on the work of others. However, the fast growth of scientifi…
SciAssess: Benchmarking LLM Proficiency in Scientific Literature Analysis
Hengxing Cai, Xiaochen Cai, Junhan Chang +20
Recent breakthroughs in Large Language Models (LLMs) have revolutionized scientific literature analysis. However, existing benchmarks fail to adequately evaluate the proficiency of…
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…
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…
Efficient sampling of high-dimensional free energy landscapes using adaptive reinforced dynamics
Dongdong Wang, Yanze Wang, Junhan Chang +3
Enhanced sampling methods such as metadynamics and umbrella sampling have become essential tools for exploring the configuration space of molecules and materials. At the same time,…
A Perspective on Deep Learning for Molecular Modeling and Simulations
Jun Zhang, Yao-Kun Lei, Zhen Zhang +6
Deep learning is transforming many areas in science, and it has great potential in modeling molecular systems. However, unlike the mature deployment of deep learning in computer vi…