Gábor Cśanyi

Eötvös Loránd University — Hungary

Materials Science · Physical Sciences

72h-index25.5kcitations317works13.42yr avg
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Research Topics

Machine Learning in Materials Science(160), Computational Drug Discovery Methods(49), X-ray Diffraction in Crystallography(45), Advanced Chemical Physics Studies(39), Protein Structure and Dynamics(29)

Publications317 total

How accurate are DFT forces? Unexpectedly large uncertainties in molecular datasets
The Journal of Chemical Physics·2025
Clustered Reverse Resumable A* Algorithm for Warehouse Robot Pathfinding
Machines·2025Open Access
A foundation model for atomistic materials chemistry
The Journal of Chemical Physics·2025· 65 citedOpen Access
Towards Routine Condensed Phase Simulations with Delta-Learned Coupled Cluster Accuracy: Application to Liquid Water
Journal of Chemical Theory and Computation·2025· 1 citedOpen Access
Bond-Network Entropy Governs Heat Transport in Coordination-Disordered Solids
Physical Review X·2025· 1 citedOpen Access
Self-consistent Coulomb interactions for machine learning interatomic potentials
Nonlinearity·2025Open Access
Optimizing Data Distribution and Kernel Performance for Efficient Training of Chemistry Foundation Models: A Case Study with MACE
2025
A predictive framework for liquid electrolytes takes root with BAMBOO
Nature Machine Intelligence·2025· 1 cited
Zero shot molecular generation via similarity kernels
Nature Communications·2025· 1 citedOpen Access
Transferability of Data Sets between Machine-Learned Interatomic Potential Algorithms
Journal of Chemical Theory and Computation·2025· 11 citedOpen Access
MACE-OFF: Short-Range Transferable Machine Learning Force Fields for Organic Molecules
Journal of the American Chemical Society·2025· 108 citedOpen Access
Resonances in reflective Hamiltonian Monte Carlo
Physical review. E·2025Open Access
Optimizing Data Distribution and Kernel Performance for Efficient Training of Chemistry Foundation Models: A Case Study with MACE
arXiv (Cornell University)·2025Open Access
Efficient exploration of reaction pathways using reaction databases and active learning
The Journal of Chemical Physics·2025· 9 citedOpen Access
Symmetry Breaking in the Superionic Phase of Silver Iodide
Physical Review Letters·2025· 1 citedOpen Access
The design space of E(3)-equivariant atom-centred interatomic potentials
Nature Machine Intelligence·2025· 118 citedOpen Access
Crash testing machine learning force fields for molecules, materials, and interfaces: molecular dynamics in the TEA challenge 2023
Chemical Science·2025· 12 citedOpen Access
Crash testing machine learning force fields for molecules, materials, and interfaces: model analysis in the TEA Challenge 2023
Chemical Science·2025· 10 citedOpen Access
Bond-Network Entropy Governs Heat Transport in Coordination-Disordered Solids
arXiv (Cornell University)·2024Open Access
Efficient Composite Infrared Spectroscopy: Combining the Double-Harmonic Approximation with Machine Learning Potentials
Journal of Chemical Theory and Computation·2024· 13 citedOpen Access
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Frequent Co-authors

Volker L. Deringer(13), Albert P. Bartók(11), Noam Bernstein(10), Michele Ceriotti(8), M. C. Payne(4), Stephen R. Elliott(4), Christoph Ortner(4), A. Miguel(3), Jörg Behler(3), James R. Kermode(3), Karsten Reuter(3), Johannes T. Margraf(3), Dávid Péter Kovács(3), Risi Kondor(2), David M. Wilkins(2), Alexander V. Shapeev(2), Aidan P. Thompson(2), Sandip De(2), Andrea C. Ferrari(2), Francesco Paesani(2)