GC
G. Csányi
cond-mat.mtrl-sciphysics.chem-phphysics.comp-phcs.LGcond-mat.stat-mechstat.MLcond-mat.dis-nncond-mat.str-elmachine learningastro-ph.IM
On Valency
published · living versionsW_7cfaaucs·v1 · currentpublished
Edge-functionalized and substitutional doped graphene nanoribbons: electronic and spin properties
with F. Cervantes-Sodi, S. Piscanec, A. C. Ferrari
1 version
W_vmvfkhks·v2published
W_8xs88vtr·v1published
W_f7cvwy93·v1published
Preprints & journals
199 papers in the corpus · 1998–2026A strategic roadmap for an atomistic machine-learning ecosystem2609.39090v2 · Jorg Behler, Michele Ceriotti, Cecilia Clementi et al.2026 · 0 citationsarXiv
Thermal conductivity predictions with foundation atomistic models.42838958 · Póta, Balázs, Ahlawat, Paramvir, Csányi, Gábor et al.2026 · 16 citationsNature communications. 2026;17(1)
A fast summation method for the DFT-D3 dispersion correction2607.15103v3 · Victoria Valeeva, Cheuk Hin Ho, Mario Geiger et al.2026 · 0 citationsarXiv
Integrating Charge Equilibration with Equivariant Machine-Learning Interatomic Potentials.42322279 · Vondrák, Martin, Baldwin, William J, Csányi, Gábor et al.2026 · 1 citationJournal of chemical theory and computation. 2026;22(13):6874-6889
Fine-tuning MLIP foundation models: strategies for accuracy and transferability2606.12704v1 · Tam'as Lajos Tompa, Eszter Varga-Umbrich, Ilyes Batatia et al.2026 · 0 citationsarXiv
How reproducible are first-principles simulations of liquid water?2605.28798v1 · Niamh ONeill, Benjamin X. Shi, William J. Baldwin et al.2026 · 1 citationarXiv
DFT Accuracy on Crystal Structure Prediction with Machine Learning Interatomic Potentials2605.28905v1 · Laurence I. Midgley, Chen Lin, J. Harry Moore et al.2026 · 0 citationsarXiv
Bottom-up synthesis of molecular nanodiamond from nanographene.42191905 · Liang, Jiaxu, Ender, Christopher P, Forero-Martinez, Nancy C et al.2026 · 2 citationsNature. 2026;655(8121):102-108
Accelerated Reaction Exploration across Scales: A Hybrid Operando and Modeling Study of Oxidation Kinetics in Monolayer Tungsten Disulfide.42160265 · Jaafar, Jad, Fan, Ye, Kazemzadeh-Atoufi, Maryam et al.2026 · 1 citationJournal of the American Chemical Society. 2026;148(21):21996-22007
Systematic Fine-Tuning of MACE Interatomic Potentials for Catalysis2605.09394v1 · Nima Karimitari, Jacob Clary, Derek Vigil-Fowler et al.2026 · 0 citationsarXiv
Atomic-scale order enables high thermal boundary conductance at -GaO/4H-SiC interfaces2604.15394v1 · Hongao Yang, Yongtao Yang, Yuanbin Liu et al.2026 · 0 citationsarXiv
Equivariant Many-body Message Passing Interatomic Potentials for Magnetic Materials2604.08143v1 · Cheuk Hin Ho, Cas van der Oord, James P. Darby et al.2026 · 1 citationarXiv
Limitations of Cluster-Trained MLIPs for Liquid Density and Diffusivity.41915790 · Svahn, Viktor, Magdău, Ioan-Bogdan, Niblett, Samuel P et al.2026 · 3 citationsJournal of chemical theory and computation. 2026;22(7):3660-3671
Design Space of Self--Consistent Electrostatic Machine Learning Interatomic Potentials2603.14700v2 · William J. Baldwin, Ilyes Batatia, Martin Vondr'ak et al.2026 · 1 citationarXiv
False Metallization in Short-Ranged Machine Learned Interatomic Potentials2603.04228v1 · Isaac J. Parker, Mandy J. Hoffmann, William J. Baldwin et al.2026 · 1 citationarXiv
Fusing Delta Learning and Machine Learned Interatomic Potentials for Efficient High Throughput Calculation of Surface Kinetic Parameters10.26434/chemrxiv.15000328/v2 · Matthew S Johnson, David H Bross, Lars Schaaf et al.2026 · 0 citationsChemRxiv
MACE-POLAR-1: A Polarisable Electrostatic Foundation Model for Molecular Chemistry2602.19411v1 · Ilyes Batatia, William J. Baldwin, Domantas Kuryla et al.2026 · 11 citationsarXiv
Better without U: Impact of Selective Hubbard U Correction on Foundational MLIPs2601.21056v1 · Thomas Warford, Fabian L. Thiemann, G'abor Cs'anyi2026 · 1 citationarXiv
Computing Solvation Free Energies of Small Molecules with Experimental Accuracy.41591329 · Harry Moore, J, Cole, Daniel J, Csányi, Gábor2026 · 17 citationsJournal of the American Chemical Society. 2026;148(5):4928-4937
Regularity Priors for the Linear Atomic Cluster Expansion2601.15072v2 · James P. Darby, Joe D. Morrow, Albert P. Bart'ok et al.2026 · 0 citationsarXiv
How accurate are DFT forces? Unexpectedly large uncertainties in molecular datasets.41369061 · Kuryla, Domantas, Berger, Fabian, Csányi, Gábor et al.2025 · 13 citationsThe Journal of chemical physics. 2025;163(22)
A foundation model for atomistic materials chemistry.41230846 · Batatia, Ilyes, Benner, Philipp, Chiang, Yuan et al.2025 · 594 citationsThe Journal of chemical physics. 2025;163(18)
Towards Routine Condensed Phase Simulations with Delta-Learned Coupled Cluster Accuracy: Application to Liquid Water.41202309 · O'Neill, Niamh, Shi, Benjamin X, Baldwin, William J et al.2025 · 14 citationsJournal of chemical theory and computation. 2025;21(22):11710-11720
Cross Learning between Electronic Structure Theories for Unifying Molecular, Surface, and Inorganic Crystal Foundation Force Fields2510.25380v1 · Ilyes Batatia, Chen Lin, Joseph Hart et al.2025 · 5 citationsarXiv
Zero shot molecular generation via similarity kernels.40593692 · Elijošius, Rokas, Zills, Fabian, Batatia, Ilyes et al.2025 · 10 citationsNature communications. 2025;16(1):5991
Transferability of Data Sets between Machine-Learned Interatomic Potential Algorithms.40470788 · Niblett, Samuel P, Kourtis, Panagiotis, Magdău, Ioan-Bogdan et al.2026 · 24 citationsJournal of chemical theory and computation. 2025;21(12):6096-6112
MACE-OFF: Short-Range Transferable Machine Learning Force Fields for Organic Molecules.40387214 · Kovács, Dávid Péter, Moore, J Harry, Browning, Nicholas J et al.2026 · 237 citationsJournal of the American Chemical Society. 2025;147(21):17598-17611
The Open Molecules 2025 (OMol25) Dataset, Evaluations, and Models2505.08762v2 · Daniel S. Levine, Muhammed Shuaibi, Evan Walter Clark Spotte-Smith et al.2025 · 80 citationsarXiv
Roadmap on Advancements of the FHI-aims Software Package2505.00125v3 · Joseph W. Abbott, Carlos Mera Acosta, Alaa Akkoush et al.2025 · 11 citationsarXiv
Optimizing Data Distribution and Kernel Performance for Efficient Training of Chemistry Foundation Models: A Case Study with MACE2504.10700v1 · Jesun Firoz, Franco Pellegrini, Mario Geiger et al.2025 · 2 citationsarXiv
Resonances in reflective Hamiltonian Monte Carlo.40411116 · Kroupa, Namu, Csányi, Gábor, Handley, Will2025 · 0 citationsPhysical review. E. 2025;111(4-2):045308
Efficient exploration of reaction pathways using reaction databases and active learning.40116310 · Kuryla, Domantas, Csányi, Gábor, van Duin, Adri C T et al.2025 · 13 citationsThe Journal of chemical physics. 2025;162(11)
A practical guide to machine learning interatomic potentials -- Status and future2503.09814v1 · Ryan Jacobs, Dane Morgan, Siamak Attarian et al.2025 · 218 citationsCurrent Opinion in Solid State and Materials Science, 35, 101214 (2025)
Crash testing machine learning force fields for molecules, materials, and interfaces: model analysis in the TEA Challenge 2023.39935506 · Poltavsky, Igor, Charkin-Gorbulin, Anton, Puleva, Mirela et al.2026 · 24 citationsChemical science. 2025;16(8):3720-3737
Many-Body Coarse-Grained Molecular Dynamics with the Atomic Cluster Expansion2502.04661v1 · Yangshuai Wang, Gabor Csanyi, Christoph Ortner2025 · 1 citationarXiv
Crash testing machine learning force fields for molecules, materials, and interfaces: molecular dynamics in the TEA challenge 2023.39911337 · Poltavsky, Igor, Puleva, Mirela, Charkin-Gorbulin, Anton et al.2026 · 28 citationsChemical science. 2025;16(8):3738-3754
Symmetry Breaking in the Superionic Phase of Silver Iodide.39913831 · Hajibabaei, Amir, Baldwin, William J, Csányi, Gábor et al.2025 · 3 citationsPhysical review letters. 2025;134(2):026306
Data-efficient fine-tuning of foundational models for first-principles quality sublimation enthalpies.39329168 · Kaur, Harveen, Della Pia, Flaviano, Batatia, Ilyes et al.2026 · 59 citationsFaraday discussions. 2025;256(0):120-138
The design space of E(3)-equivariant atom-centred interatomic potentials.39877429 · Batatia, Ilyes, Batzner, Simon, Kovács, Dávid Péter et al.2026 · 202 citationsNature machine intelligence. 2025;7(1):56-67
Bond-Network Entropy Governs Heat Transport in Coordination-Disordered Solids2412.12753v3 · Kamil Iwanowski, G'abor Cs'anyi, Michele Simoncelli2024 · 6 citationsarXiv
17O NMR Spectroscopy Reveals CO2 Speciation and Dynamics in Hydroxide-Based Carbon Capture Materials.39565330 · Rhodes, Benjamin J, Schaaf, Lars L, Zick, Mary E et al.2025 · 22 citationsChemphyschem : a European journal of chemical physics and physical chemistry. 2025;26(5):e202400941
Efficient Composite Infrared Spectroscopy: Combining the Double-Harmonic Approximation with Machine Learning Potentials.39665618 · Pracht, Philipp, Pillai, Yuthika, Kapil, Venkat et al.2026 · 17 citationsJournal of chemical theory and computation. 2024;20(24):10986-11004
Workflows for Artificial Intelligence10.26434/chemrxiv-2024-vw06p · Jörg Behler, Gábor Csányi, Lucas Foppa et al.2024 · 0 citationsChemRxiv
Random Spin Committee Approach For Smooth Interatomic Potentials2410.16252v2 · Vlad Carare, Volker L. Deringer, G'abor Cs'anyi2024 · 0 citationsarXiv
Accurate Crystal Structure Prediction of New 2D Hybrid Organic-Inorganic Perovskites.39344597 · Karimitari, Nima, Baldwin, William J, Muller, Evan W et al.2026 · 23 citationsJournal of the American Chemical Society. 2024;146(40):27392-27404
Transferability of datasets between Machine-Learning Interaction Potentials2409.05590v1 · Samuel P. Niblett, Panagiotis Kourtis, Ioan-Bogdan Magdau et al.2024 · 5 citationsarXiv
Guest editorial: Special Topic on software for atomistic machine learning.39120027 · Rupp, Matthias, Küçükbenli, Emine, Csányi, Gábor2024 · 4 citationsThe Journal of chemical physics. 2024;161(6)
Self-consistent Coulomb interactions for machine learning interatomic potentials2406.10915v1 · Jack Thomas, William J. Baldwin, G'abor Cs'anyi et al.2024 · 4 citationsarXiv
Toward transferable empirical valence bonds: Making classical force fields reactive.38526105 · Allen, Alice E A, Csányi, Gábor2024 · 4 citationsThe Journal of chemical physics. 2024;160(12)
Benchmarking of machine learning interatomic potentials for reactive hydrogen dynamics at metal surfaces2403.15334v1 · Wojciech G. Stark, Cas van der Oord, Ilyes Batatia et al.2024 · 26 citationsarXiv
Career total: at least 527 works. 199 are in this corpus.Showing the 50 most recent.
Profile built from the corpus for this byline.
Author records are still filling in while the Hub is in alpha. If this is your page, you'll be able to claim it soon. Spot a mistake? Tell us.