982 citations · 1.9k across the 22 of their papers we have counts for
22 papers
PRL-Bench: A Comprehensive Benchmark Evaluating LLMs' Capabilities in Frontier Physics Research
Tingjia Miao, Wenkai Jin, Muhua Zhang +19
The paradigm of agentic science requires AI systems to conduct robust reasoning and engage in long-horizon, autonomous exploration. However, current scientific benchmarks remain co…
EvoMaster: A Foundational Evolving Agent Framework for Agentic Science at Scale
Xinyu Zhu, Yuzhu Cai, Zexi Liu +20
The convergence of large language models and agents is catalyzing a new era of scientific discovery: Agentic Science. However, common agent infrastructure is repeatedly rebuilt acr…
Multi-Task Fine-Tuning Enables Robust Out-of-Distribution Generalization in Atomistic Models
Chengqian Zhang, Duo Zhang, Anyang Peng +7
Accurate de novo molecular and materials design requires structure-property models that generalize beyond known regimes. Although pretrained atomistic models achieve strong in-dist…
Toward Ultra-Long-Horizon Agentic Science: Cognitive Accumulation for Machine Learning Engineering
Xinyu Zhu, Yuzhu Cai, Zexi Liu +8
The advancement of artificial intelligence toward agentic science is currently bottlenecked by the challenge of ultra-long-horizon autonomy, the ability to sustain strategic cohere…
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…
PhysMaster: Building an Autonomous AI Physicist for Theoretical and Computational Physics Research
Tingjia Miao, Wenkai Jin, Jinxin Tan +22
Advances in LLM reasoning and tool use have enabled agentic science, yet frontier theoretical and computational physics remains challenging because research requires deep domain ex…
Transfer learning discovery of molecular modulators for perovskite solar cells
Haoming Yan, Xinyu Chen, Yanran Wang +10
The discovery of effective molecular modulators is essential for advancing perovskite solar cells (PSCs), but the research process is hindered by the vastness of chemical space and…
SciMaster: Towards General-Purpose Scientific AI Agents, Part I. X-Master as Foundation: Can We Lead on Humanity's Last Exam?
Jingyi Chai, Shuo Tang, Rui Ye +8
The rapid advancements of AI agents have ignited the long-held ambition of leveraging them to accelerate scientific discovery. Achieving this goal requires a deep understanding of…
DiffLiB: High-fidelity differentiable modeling of lithium-ion batteries and efficient gradient-based parameter identification
Weipeng Xu, Kaiqi Yang, Yuzhi Zhang +3
The physics-based Doyle-Fuller-Newman (DFN) model, widely adopted for its precise electrochemical modeling, stands out among various simulation models of lithium-ion batteries (LIB…
ABACUS: An Electronic Structure Analysis Package for the AI Era
Weiqing Zhou, Daye Zheng, Qianrui Liu +55
ABACUS (Atomic-orbital Based Ab-initio Computation at USTC) is an open-source software for first-principles electronic structure calculations and molecular dynamics simulations. It…
A Pre-trained Deep Potential Model for Sulfide Solid Electrolytes with Broad Coverage and High Accuracy
Ruoyu Wang, Mingyu Guo, Yuxiang Gao +7
Solid electrolytes with fast ion transport are one of the key challenges for solid state lithium metal batteries. To improve ion conductivity, chemical doping has been the most eff…
Uni-ELF: A Multi-Level Representation Learning Framework for Electrolyte Formulation Design
Boshen Zeng, Sian Chen, Xinxin Liu +7
Advancements in lithium battery technology heavily rely on the design and engineering of electrolytes. However, current schemes for molecular design and recipe optimization of elec…
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…
Universal interatomic potential for perovskite oxides
Jing Wu, Jiyuan Yang, Yuan-Jinsheng Liu +5
With their celebrated structural and chemical flexibility, perovskite oxides have served as a highly adaptable material platform for exploring emergent phenomena arising from the i…
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…
Deep Learning of Accurate Force Field of Ferroelectric HfO
Jing Wu, Yuzhi Zhang, Linfeng Zhang +1
The discovery of ferroelectricity in HfO-based thin films opens up new opportunities for using this silicon-compatible ferroelectric to realize low-power logic circuits and hig…
Accurate Deep Potential model for the Al-Cu-Mg alloy in the full concentration space
Wanrun Jiang, Yuzhi Zhang, Linfeng Zhang +1
Combining first-principles accuracy and empirical-potential efficiency for the description of the potential energy surface (PES) is the philosopher's stone for unraveling the natur…
DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models
Yuzhi Zhang, Haidi Wang, Weijie Chen +4
In recent years, promising deep learning based interatomic potential energy surface (PES) models have been proposed that can potentially allow us to perform molecular dynamics simu…
Warm dense matter simulation via electron temperature dependent deep potential molecular dynamics
Yuzhi Zhang, Chang Gao, Linfeng Zhang +2
Simulating warm dense matter that undergoes a wide range of temperatures and densities is challenging. Predictive theoretical models, such as quantum-mechanics-based first-principl…