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G\'abor Cs\'anyi

Identifiers

  • name variant G\'abor Cs\'anyi 0.60 · backfill

Papers (38)

  1. Fine-tuning MLIP foundation models: strategies for accuracy and transferability physics.chem-ph · 2026 · author #6
  2. DFT Accuracy on Crystal Structure Prediction with Machine Learning Interatomic Potentials physics.chem-ph · 2026 · author #11
  3. Systematic Fine-Tuning of MACE Interatomic Potentials for Catalysis physics.chem-ph · 2026 · author #5
  4. Atomic-scale order enables high thermal boundary conductance at $\beta$-Ga$_2$O$_3$/4H-SiC interfaces cond-mat.mtrl-sci · 2026 · author #10
  5. Equivariant Many-body Message Passing Interatomic Potentials for Magnetic Materials cond-mat.mtrl-sci · 2026 · author #13
  6. Roadmap on Advancements of the FHI-aims Software Package cond-mat.mtrl-sci · 2025 · author #24
  7. A foundation model for atomistic materials chemistry physics.chem-ph · 2023 · author #88
  8. Efficiency, Accuracy, and Transferability of Machine Learning Potentials: Application to Dislocations and Cracks in Iron cond-mat.mtrl-sci · 2023 · author #2
  9. Machine-learned Interatomic Potentials for Alloys and Alloy Phase Diagrams cond-mat.mtrl-sci · 2019 · author #6
  10. Machine-learning of atomic-scale properties based on physical principles physics.comp-ph · 2019 · author #3
  11. Quantifying Chemical Structure and Atomic Energies in Amorphous Silicon Networks cond-mat.mtrl-sci · 2018 · author #3
  12. Equation of state of fluid methane from first principles with machine learning potentials physics.chem-ph · 2018 · author #6
  13. Machine-learned multi-system surrogate models for materials prediction cond-mat.mtrl-sci · 2018 · author #7
  14. Growth Mechanism and Origin of High $sp^3$ Content in Tetrahedral Amorphous Carbon cond-mat.mtrl-sci · 2018 · author #5
  15. Realistic atomistic structure of amorphous silicon from machine-learning-driven molecular dynamics cond-mat.mtrl-sci · 2018 · author #9
  16. Comparison of permutationally invariant polynomials, neural networks, and Gaussian approximation potentials in representing water interactions through many-body expansions physics.chem-ph · 2018 · author #5
  17. Gaussian approximation potential modeling of lithium intercalation in carbon nanostructures cond-mat.mtrl-sci · 2017 · author #6
  18. Constant-pressure nested sampling with atomistic dynamics cond-mat.stat-mech · 2017 · author #5
  19. Data-driven learning of total and local energies in elemental boron cond-mat.mtrl-sci · 2017 · author #3
  20. A Machine Learning Potential for Graphene cond-mat.mtrl-sci · 2017 · author #2
  21. Symmetry-Adapted Machine-Learning for Tensorial Properties of Atomistic Systems cond-mat.mtrl-sci · 2017 · author #3
  22. Polytypism in the ground state structure of the Lennard-Jonesium cond-mat.mtrl-sci · 2017 · author #5
  23. Machine-learning based interatomic potential for amorphous carbon cond-mat.mtrl-sci · 2016 · author #2
  24. Structural Simplicity as a Restraint on the Structure of Amorphous Silicon cond-mat.mtrl-sci · 2016 · author #5
  25. Determining pressure-temperature phase diagrams of materials cond-mat.mtrl-sci · 2015 · author #5
  26. The Adaptive Buffered Force QM/MM method in the CP2K and AMBER software packages physics.chem-ph · 2014 · author #7
  27. Accuracy and transferability of GAP models for tungsten cond-mat.mtrl-sci · 2014 · author #3
  28. Free energy surface reconstruction from umbrella samples using Gaussian process regression II: Multiple collective variables cond-mat.stat-mech · 2013 · author #3
  29. Free energy surface reconstruction from umbrella samples using Gaussian process regression cond-mat.stat-mech · 2013 · author #3
  30. On representing chemical environments physics.comp-ph · 2012 · author #3
  31. Nested sampling for materials: the case of hard spheres cond-mat.stat-mech · 2012 · author #3
  32. Diffusive Nested Sampling stat.CO · 2009 · author #3
  33. Gaussian Approximation Potentials: the accuracy of quantum mechanics, without the electrons physics.comp-ph · 2009 · author #4
  34. Efficient sampling of atomic configurational spaces cond-mat.stat-mech · 2009 · author #3
  35. Polynomial epidemics and clustering in contact networks q-bio.PE · 2004 · author #2
  36. The fractal/small-world dichotomy in real-world networks cond-mat.stat-mech · 2004 · author #1
  37. Chemically active substitutional nitrogen impurity in carbon nanotubes cond-mat.mtrl-sci · 2003 · author #2
  38. Improved tensor-product expansions for the two-particle density matrix cond-mat.mtrl-sci · 2001 · author #1

Mentions

  • 1802.00564 #5 · arxiv_oai · confidence 0.70 G\'abor Cs\'anyi
  • 1804.07463 #5 · arxiv_oai · confidence 0.70 G\'abor Cs\'anyi
  • 1710.10475 #3 · arxiv_oai · confidence 0.70 G\'abor Cs\'anyi
  • 1712.04472 #6 · arxiv_oai · confidence 0.70 G\'abor Cs\'anyi
  • 1803.02802 #9 · arxiv_oai · confidence 0.70 G\'abor Cs\'anyi
  • 1710.04187 #2 · arxiv_oai · confidence 0.70 G\'abor Cs\'anyi
  • 1709.06757 #3 · arxiv_oai · confidence 0.70 G\'abor Cs\'anyi
  • 1710.11085 #5 · arxiv_oai · confidence 0.70 G\'abor Cs\'anyi
  • 1705.01751 #5 · arxiv_oai · confidence 0.70 G\'abor Cs\'anyi
  • 1609.00668 #5 · arxiv_oai · confidence 0.70 G\'abor Cs\'anyi
  • 1611.03277 #2 · arxiv_oai · confidence 0.70 G\'abor Cs\'anyi
  • 1503.03404 #5 · arxiv_oai · confidence 0.70 G\'abor Cs\'anyi
  • 1209.3140 #3 · arxiv_oai · confidence 0.70 G\'abor Cs\'anyi
  • 0910.1019 #4 · arxiv_oai · confidence 0.70 G\'abor Cs\'anyi
  • 1409.5218 #7 · arxiv_oai · confidence 0.70 G\'abor Cs\'anyi
  • 1405.4370 #3 · arxiv_oai · confidence 0.70 G\'abor Cs\'anyi
  • 1312.4420 #3 · arxiv_oai · confidence 0.70 G\'abor Cs\'anyi
  • 1312.4419 #3 · arxiv_oai · confidence 0.70 G\'abor Cs\'anyi
  • 1208.1721 #3 · arxiv_oai · confidence 0.70 G\'abor Cs\'anyi
  • 0912.2380 #3 · arxiv_oai · confidence 0.70 G\'abor Cs\'anyi
  • 0906.3544 #3 · arxiv_oai · confidence 0.70 G\'abor Cs\'anyi
  • cond-mat/0406070 #1 · arxiv_oai · confidence 0.70 G\'abor Cs\'anyi
  • cond-mat/0107536 #1 · arxiv_oai · confidence 0.70 G\'abor Cs\'anyi
  • q-bio/0406013 #2 · arxiv_oai · confidence 0.70 G\'abor Cs\'anyi
  • cond-mat/0301230 #2 · arxiv_oai · confidence 0.70 G\'abor Cs\'anyi
  • 2606.12704 #6 · arxiv_oai · confidence 0.70 G\'abor Cs\'anyi
  • 2307.10072 #2 · arxiv_oai · confidence 0.70 G\'abor Cs\'anyi
  • 1503.03404 #5 · backfill · confidence 0.70 G\'abor Cs\'anyi
  • 1409.5218 #7 · backfill · confidence 0.70 G\'abor Cs\'anyi
  • 1405.4370 #3 · backfill · confidence 0.70 G\'abor Cs\'anyi
  • 1312.4420 #3 · backfill · confidence 0.70 G\'abor Cs\'anyi
  • 1312.4419 #3 · backfill · confidence 0.70 G\'abor Cs\'anyi
  • 2605.28905 #11 · arxiv_oai · confidence 0.70 G\'abor Cs\'anyi
  • 1209.3140 #3 · backfill · confidence 0.70 G\'abor Cs\'anyi
  • 1208.1721 #3 · backfill · confidence 0.70 G\'abor Cs\'anyi
  • 2401.00096 #88 · arxiv_oai · confidence 0.70 G\'abor Cs\'anyi
  • 0912.2380 #3 · backfill · confidence 0.70 G\'abor Cs\'anyi
  • 0910.1019 #4 · backfill · confidence 0.70 G\'abor Cs\'anyi
  • 0906.3544 #3 · backfill · confidence 0.70 G\'abor Cs\'anyi

Frequent Coauthors