Articlenpj computational materials2024
DPA-2: a large atomic model as a multi-task learner.
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Article in npj computational materials, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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32citing papers in PubMed
–field-weighted citation impact
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
32 citing papers in PubMed.
- Machine Learning Potential for Calcium Silicate Hydrates With Broad Compositional and Structural Diversity.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Systematic trends in water properties across Jacob's ladder density functionals.Digital discovery · 2026Article
- Boron-assisted synthesis of compositionally complex amorphous oxides via short-range-order-constrained generative design.Science advances · 2026Article
- DL_FFLUX Refactored: Accelerating Quantum Chemical Topology Simulations With Optimized Parallelization.Journal of computational chemistry · 2026Article
- Data-Efficient and Fast Machine Learning Molecular Dynamics through Integrated Active Learning and Knowledge Distillation.Journal of chemical theory and computation · 2026Article
- Coverage-Dependent Free-Energy Bottlenecks in CO Adsorption and Desorption on Ru(0001).JACS Au · 2026Article
- Solvent-temperature coupled hydride transfer controls product selectivity in the HSiRChemical science · 2026Article
- Transfer Learning Meets Embedded Correlated Wavefunction Theory for Chemically Accurate Molecular Simulations: Application to Calcium Carbonate Ion Pairing.Journal of chemical theory and computation · 2026Article
- Siamese foundation models for crystal structure prediction.Nature communications · 2026Article
- Reactive Machine Learning Interatomic Potentials for Chemistry and Materials Science.Chemical reviews · 2026Review
- Li-P-S Electrolyte Materials as a Benchmark for Machine-Learned Interatomic Potentials.Journal of chemical theory and computation · 2026Article
- Mechanistic Analysis of Large Atomic Models of Molten Salt.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Machine Learning Potentials for Inorganic and Hybrid Lead Halide Perovskites: From Phase Stability to Defects and Interfaces.ACS applied materials & interfaces · 2026Review
- Machine Learning Discovery of Record-Low Lattice Thermal Conductivity in Double Perovskites.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Computational discovery of high-temperature superconducting ternary hydrides via deep learning.National science review · 2026Article
- Node-equivariant message passing for efficient and accurate machine learning interatomic potentials.Chemical science · 2026Article
- Flexible uncertainty calibration for machine-learned interatomic potentials.npj computational materials · 2026Article
- Domain oriented universal machine learning potential enables fast exploration of chemical space of battery electrolytes.Nature communications · 2025Article
- Molecular Simulation of Hydrogen Systems: From Properties and Methods to Applications and Future Directions.Chemical reviews · 2025Review
- A Machine Learning Interatomic Potential Data Set and Model for Catalysis with Local Fine-Tuning to Chemical Accuracy.JACS Au · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
43 authors.
Duo ZhangAI for Science Institute, Beijing 100080, P. R. China.
Xinzijian LiuAI for Science Institute, Beijing 100080, P. R. China.
Xiangyu ZhangState Key Lab of Processors, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100871, P.R. China.
Chengqian ZhangDP Technology, Beijing 100080, P. R. China.
Chun CaiAI for Science Institute, Beijing 100080, P. R. China.
Hangrui BiAI for Science Institute, Beijing 100080, P. R. China.
Yiming DuState Key Lab of Processors, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100871, P.R. China.
Xuejian QinNingbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, P.R. China.
Anyang PengAI for Science Institute, Beijing 100080, P. R. China.
Jiameng HuangDP Technology, Beijing 100080, P. R. China.
Bowen LiShanghai Engineering Research Center of Molecular Therapeutics & New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200062, P.R. China.
Yifan ShanNingbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, P.R. China.
Jinzhe ZengLaboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, New Jersey 08854, USA.
Yuzhi ZhangDP Technology, Beijing 100080, P. R. China.
Siyuan LiuDP Technology, Beijing 100080, P. R. China.
Yifan LiDepartment of Chemistry, Princeton University, Princeton, New Jersey 08540, USA.
Junhan ChangDP Technology, Beijing 100080, P. R. China.
Xinyan WangDP Technology, Beijing 100080, P. R. China.
Shuo ZhouDP Technology, Beijing 100080, P. R. China.
Jianchuan LiuSchool of Electrical Engineering and Electronic Information, Xihua University, Chengdu, 610039, P.R. China.
Xiaoshan LuoState Key Laboratory of Superhard Materials, College of Physics, Jilin University, Changchun 130012, P.R. China.
Zhenyu WangKey Laboratory of Material Simulation Methods & Software of Ministry of Education, College of Physics, Jilin University, Changchun, 130012, P.R. China.
Wanrun JiangAI for Science Institute, Beijing 100080, P. R. China.
Jing WuKey Laboratory for Quantum Materials of Zhejiang Province, Department of Physics, School of Science, Westlake University, Hangzhou, Zhejiang 310030, P.R. China.
Yudi YangKey Laboratory for Quantum Materials of Zhejiang Province, Department of Physics, School of Science, Westlake University, Hangzhou, Zhejiang 310030, P.R. China.
Jiyuan YangKey Laboratory for Quantum Materials of Zhejiang Province, Department of Physics, School of Science, Westlake University, Hangzhou, Zhejiang 310030, P.R. China.
Manyi YangAtomistic Simulations, Italian Institute of Technology, 16156 Genova, Italy.
Fu-Qiang GongState Key Laboratory of Physical Chemistry of Solid Surface, iChEM, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, P.R. China.
Linshuang ZhangDP Technology, Beijing 100080, P. R. China.
Mengchao ShiDP Technology, Beijing 100080, P. R. China.
Fu-Zhi DaiAI for Science Institute, Beijing 100080, P. R. China.
Darrin M YorkLaboratory for Biomolecular Simulation Research, Institute for Quantitative Biomedicine and Department of Chemistry and Chemical Biology, Rutgers University, Piscataway, New Jersey 08854, USA.
Shi LiuKey Laboratory for Quantum Materials of Zhejiang Province, Department of Physics, School of Science, Westlake University, Hangzhou, Zhejiang 310030, P.R. China.
Tong ZhuShanghai Engineering Research Center of Molecular Therapeutics & New Drug Development, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200062, P.R. China.
Zhicheng ZhongNingbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo 315201, P.R. China.
Jian LvKey Laboratory of Material Simulation Methods & Software of Ministry of Education, College of Physics, Jilin University, Changchun, 130012, P.R. China.
Jun ChengState Key Laboratory of Physical Chemistry of Solid Surface, iChEM, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen, 361005, P.R. China.
Weile JiaState Key Lab of Processors, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100871, P.R. China.
Mohan ChenAI for Science Institute, Beijing 100080, P. R. China.
Guolin KeDP Technology, Beijing 100080, P. R. China.
E WeinanAI for Science Institute, Beijing 100080, P. R. China.
Linfeng ZhangAI for Science Institute, Beijing 100080, P. R. China.
Han WangHEDPS, CAPT, College of Engineering, Peking University, Beijing 100871, P.R. China.
Funding
Next-generation integrated quantum force fields for biomedical applicationsR01GM107485 · NIGMS · RUTGERS, THE STATE UNIV OF N.J. · PI Darrin M York · 2015 to 2026
$3.8MNIGMS NIH HHS R01 GM107485
Abstract
The rapid advancements in artificial intelligence (AI) are catalyzing transformative changes in atomic modeling, simulation, and design. AI-driven potential energy models have demonstrated the capability to conduct large-scale, long-duration simulations with the accuracy of
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