G\'abor Cs\'anyi
Identifiers
- name variant G\'abor Cs\'anyi 0.60 · backfill
Papers (36)
- DFT Accuracy on Crystal Structure Prediction with Machine Learning Interatomic Potentials physics.chem-ph · 2026 · author #11
- Systematic Fine-Tuning of MACE Interatomic Potentials for Catalysis physics.chem-ph · 2026 · author #5
- Atomic-scale order enables high thermal boundary conductance at $\beta$-Ga$_2$O$_3$/4H-SiC interfaces cond-mat.mtrl-sci · 2026 · author #10
- Equivariant Many-body Message Passing Interatomic Potentials for Magnetic Materials cond-mat.mtrl-sci · 2026 · author #13
- Roadmap on Advancements of the FHI-aims Software Package cond-mat.mtrl-sci · 2025 · author #24
- A foundation model for atomistic materials chemistry physics.chem-ph · 2023 · author #88
- Machine-learned Interatomic Potentials for Alloys and Alloy Phase Diagrams cond-mat.mtrl-sci · 2019 · author #6
- Machine-learning of atomic-scale properties based on physical principles physics.comp-ph · 2019 · author #3
- Quantifying Chemical Structure and Atomic Energies in Amorphous Silicon Networks cond-mat.mtrl-sci · 2018 · author #3
- Equation of state of fluid methane from first principles with machine learning potentials physics.chem-ph · 2018 · author #6
- Machine-learned multi-system surrogate models for materials prediction cond-mat.mtrl-sci · 2018 · author #7
- Growth Mechanism and Origin of High $sp^3$ Content in Tetrahedral Amorphous Carbon cond-mat.mtrl-sci · 2018 · author #5
- Realistic atomistic structure of amorphous silicon from machine-learning-driven molecular dynamics cond-mat.mtrl-sci · 2018 · author #9
- 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
- Gaussian approximation potential modeling of lithium intercalation in carbon nanostructures cond-mat.mtrl-sci · 2017 · author #6
- Constant-pressure nested sampling with atomistic dynamics cond-mat.stat-mech · 2017 · author #5
- Data-driven learning of total and local energies in elemental boron cond-mat.mtrl-sci · 2017 · author #3
- A Machine Learning Potential for Graphene cond-mat.mtrl-sci · 2017 · author #2
- Symmetry-Adapted Machine-Learning for Tensorial Properties of Atomistic Systems cond-mat.mtrl-sci · 2017 · author #3
- Polytypism in the ground state structure of the Lennard-Jonesium cond-mat.mtrl-sci · 2017 · author #5
- Machine-learning based interatomic potential for amorphous carbon cond-mat.mtrl-sci · 2016 · author #2
- Structural Simplicity as a Restraint on the Structure of Amorphous Silicon cond-mat.mtrl-sci · 2016 · author #5
- Determining pressure-temperature phase diagrams of materials cond-mat.mtrl-sci · 2015 · author #5
- The Adaptive Buffered Force QM/MM method in the CP2K and AMBER software packages physics.chem-ph · 2014 · author #7
- Accuracy and transferability of GAP models for tungsten cond-mat.mtrl-sci · 2014 · author #3
- Free energy surface reconstruction from umbrella samples using Gaussian process regression II: Multiple collective variables cond-mat.stat-mech · 2013 · author #3
- Free energy surface reconstruction from umbrella samples using Gaussian process regression cond-mat.stat-mech · 2013 · author #3
- On representing chemical environments physics.comp-ph · 2012 · author #3
- Nested sampling for materials: the case of hard spheres cond-mat.stat-mech · 2012 · author #3
- Diffusive Nested Sampling stat.CO · 2009 · author #3
- Gaussian Approximation Potentials: the accuracy of quantum mechanics, without the electrons physics.comp-ph · 2009 · author #4
- Efficient sampling of atomic configurational spaces cond-mat.stat-mech · 2009 · author #3
- Polynomial epidemics and clustering in contact networks q-bio.PE · 2004 · author #2
- The fractal/small-world dichotomy in real-world networks cond-mat.stat-mech · 2004 · author #1
- Chemically active substitutional nitrogen impurity in carbon nanotubes cond-mat.mtrl-sci · 2003 · author #2
- Improved tensor-product expansions for the two-particle density matrix cond-mat.mtrl-sci · 2001 · author #1
Mentions
- 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
- Albert P. Bart\'ok 9 shared papers
- Noam Bernstein 8 shared papers
- Volker L. Deringer 7 shared papers
- Michele Ceriotti 5 shared papers
- L\'ivia B. P\'artay 4 shared papers
- Christoph Ortner 3 shared papers
- Clare P. Grey 3 shared papers
- Livia B. P\'artay 3 shared papers
- Stephen R. Elliott 3 shared papers
- Alexander V. Shapeev 2 shared papers
- Andrea Grisafi 2 shared papers
- Andreas W. G\"otz 2 shared papers
- Angelos Michaelides 2 shared papers
- Bal\'azs Szendr\"oi 2 shared papers
- Cas van der Oord 2 shared papers
- Chen Lin 2 shared papers
- Cheuk Hin Ho 2 shared papers
- Chris J. Pickard 2 shared papers
- Christopher Sutton 2 shared papers
- Conrad W. Rosenbrock 2 shared papers