Gábor Csányi
researcher
Facts
10 properties- Instance of
- human
- Occupation
- computational physicist
- Employer
- University of Cambridge[2]
- Educated at
- Massachusetts Institute of Technology (?–2001)[3]
- Award received
- Fellow of the Royal Society[5]
- Doctoral advisor
- Tomás Arias[4]
- Imported from
- wholeness_audit_2026-05-02
🏷️ Also known as
en Gábor Csányi
hu Csányi Gábor
🔖 Identifiers · 5
- ORCID iD
- 0000-0002-8180-2034
- OpenAlex ID
- A5025442671, A5110933824, A5123944415, A5132558843, A5134789745, A5135869560, A5136048015, A5136597693
- Exact match
- www.eng.cam.ac.uk
- Google Scholar author ID
- q39javYAAAAJ
- MR Author ID
- 952708
📜 Data licence
Source: OpenAlex (openalex.org), snapshot of 2026-09-23. CC0 1.0 Universal. CC0 1.0 Universal · OpenAlex
🔗 Connections
Author of 37
Accurate energy barriers for catalytic reaction pathways: an automatic training protocol for machine learning force fields,
ACEpotentials.jl: A Julia implementation of the atomic cluster expansion,
A general-purpose machine-learning force field for bulk and nanostructured phosphorus,
A Hybrid Machine Learning Approach for Structure Stability Prediction in Molecular Co-crystal Screenings,
Atomic Permutationally Invariant Polynomials for Fitting Molecular Force\n Fields,
Bayesian inference of the spatial distributions of material properties,
Benchmarking of machine learning interatomic potentials for reactive hydrogen dynamics at metal surfaces,
Combining phonon accuracy with high transferability in Gaussian approximation potential models.
+29 more
← Authored by 37
Accurate energy barriers for catalytic reaction pathways: an automatic training protocol for machine learning force fields,
ACEpotentials.jl: A Julia implementation of the atomic cluster expansion,
A general-purpose machine-learning force field for bulk and nanostructured phosphorus,
A Hybrid Machine Learning Approach for Structure Stability Prediction in Molecular Co-crystal Screenings,
Atomic Permutationally Invariant Polynomials for Fitting Molecular Force\n Fields,
Bayesian inference of the spatial distributions of material properties,
Benchmarking of machine learning interatomic potentials for reactive hydrogen dynamics at metal surfaces,
Combining phonon accuracy with high transferability in Gaussian approximation potential models.
+29 more
Affiliated with 3
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Summary
Gábor Csányi worked as a computational physicist[1].
FAQs
What did Gábor Csányi do for work?
Gábor Csányi worked as computational physicist[1].
Where did Gábor Csányi go to school?
Gábor Csányi was educated at Massachusetts Institute of Technology[3].
What awards did Gábor Csányi receive?
Honors received include Fellow of the Royal Society[5].