Gábor Csányi

1. A foundation model for atomistic materials chemistry
   2023/12/29 by Ilyes Batatia, Batatia, Ilyes, Philipp Benner +182 · 5 voices · 139 citations
   Materials Science · Decision Sciences · #physics.chem-ph #cond-mat.mtrl-sci
2. The Open Molecules 2025 (OMol25) Dataset, Evaluations, and Models
   2025/05/13 by Daniel S. Levine, Muhammed Shuaibi, Levine, Daniel S. +43 · 6 voices · 25 citations
   #physics.chem-ph
3. The Design Space of E(3)-Equivariant Atom-Centered Interatomic Potentials
   2022/05/13 by Ilyes Batatia, Simon Batzner, Batatia, Ilyes +15 · 58 citations
   Materials Science · Physics and Astronomy · #Advanced Chemical Physics Studies #Chemical Physics (physics.chem-ph) #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning in Materials Science #Materials Science (cond-mat.mtrl-sci) #X-ray Diffraction in Crystallography
4. Gaussian approximation potentials: A brief tutorial introduction
   2015/04/27 by Albert P. Bartók, Gábor Csányi, Gábor Cśanyi · 43 citations
   Materials Science · Biochemistry, Genetics and Molecular Biology · Computer Science · #Machine Learning in Materials Science #Protein Structure and Dynamics #Computational Drug Discovery Methods
5. MACE-OFF: Transferable Short Range Machine Learning Force Fields for Organic Molecules
   2023/12/23 by Dávid Péter Kovács, J. Harry Moore, Kovács, Dávid Péter +20 · 1 voice · 41 citations
   Biochemistry, Genetics and Molecular Biology · Chemistry · Materials Science · #Machine Learning in Materials Science #Mass Spectrometry Techniques and Applications #Protein Structure and Dynamics #physics.chem-ph
6. Machine-learned Interatomic Potentials for Alloys and Alloy Phase\n Diagrams
   2019/06/18 by Conrad W. Rosenbrock, Konstantin Gubaev, Rosenbrock, Conrad W. +11 · 8 citations
   Materials Science · #Computational Physics (physics.comp-ph) #Electron and X-Ray Spectroscopy Techniques #FOS: Physical sciences #Machine Learning in Materials Science #Materials Science (cond-mat.mtrl-sci) #X-ray Diffraction in Crystallography
7. Nested sampling for physical scientists
   2022/05/26 by G. Ashton, Greg Ashton, Noam Bernstein +30 · 1 voice · 8 citations
   Computer Science · Mathematics · Physics and Astronomy · #Cosmology and Gravitation Theories #Gaussian Processes and Bayesian Inference #Statistical Mechanics and Entropy #astro-ph.CO #astro-ph.IM #cond-mat.mtrl-sci #hep-ph #stat.CO
8. Thermal Conductivity Predictions with Foundation Atomistic Models
   2024/08/01 by Balázs Póta, Póta, Balázs, Paramvir Ahlawat +5 · 12 citations
   Chemistry · Materials Science · #Advanced Physical and Chemical Molecular Interactions #Applied Physics (physics.app-ph) #FOS: Physical sciences #Machine Learning in Materials Science #Materials Science (cond-mat.mtrl-sci) #Thermal properties of materials
9. Diffusive nested sampling
   2010/08/24 by Brendon J. Brewer, Lívia B. Pártay, Livia B. Pártay +2 · 2 citations
   Mathematics · Physics and Astronomy · #Markov Chains and Monte Carlo Methods #Statistical Mechanics and Entropy #Theoretical and Computational Physics
10. Roadmap on Advancements of the FHI-aims Software Package
    2025/04/30 by Joseph W. Abbott, Carlos Mera Acosta, Abbott, Joseph W. +405 · 3 voices · 7 citations
    #cond-mat.mtrl-sci #physics.chem-ph
11. Atomistic fracture in bcc iron revealed by active learning of Gaussian approximation potential
    2022/08/11 by Lei Zhang, Gábor Csányi, Zhang, Lei +5 · 4 citations
    Engineering · Materials Science · #FOS: Physical sciences #Hydrogen embrittlement and corrosion behaviors in metals #Materials Science (cond-mat.mtrl-sci) #Microstructure and Mechanical Properties of Steels #Microstructure and mechanical properties
12. Dynamic Local Structure in Caesium Lead Iodide: Spatial Correlation and Transient Domains
    2023/04/10 by William M. Baldwin, Xia Liang, Baldwin, William +19 · 2 citations
    Engineering · Materials Science · #Advanced Thermoelectric Materials and Devices #FOS: Physical sciences #Machine Learning in Materials Science #Materials Science (cond-mat.mtrl-sci) #Perovskite Materials and Applications
13. Equivariant Matrix Function Neural Networks
    2023/10/16 by Ilyes Batatia, Batatia, Ilyes, Lars L. Schaaf +9 · 1 citation
    Computer Science · Materials Science · Physics and Astronomy · #Chemical Physics (physics.chem-ph) #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning in Materials Science #Materials Science (cond-mat.mtrl-sci) #Neural Networks and Reservoir Computing #Quantum many-body systems
14. How Accurate Are DFT Forces? Unexpectedly Large Uncertainties in Molecular Datasets
    2025/10/22 by Domantas Kuryla, Kuryla, Domantas, Fabian Berger +5 · 1 voice · 3 citations
    #physics.chem-ph
15. A universal preconditioner for simulating condensed phase materials
    2016/04/26 by David Packwood, James Kermode, Letif Mones +5 · 12 citations
    Materials Science · Physics and Astronomy · #Machine Learning in Materials Science #Advanced Chemical Physics Studies #Block Copolymer Self-Assembly
16. A fast summation method for the DFT-D3 dispersion correction
    2026/07/16 by Victoria Valeeva, Cheuk Hin Ho, Mario Geiger +4
    #physics.comp-ph