Gábor Csányi

发表

Gaussian Processes: A Method for Automatic QSAR Modeling of ADME Properties

Gábor Csányi, Matthew D. Segall, Olga Obrezanova, 2007, J. Chem. Inf. Model..

Gaussian approximation potentials: the accuracy of quantum mechanics, without the electrons.

R. Kondor, M. Payne, Gábor Csányi, 2009, Physical review letters.

On representing chemical environments

R. Kondor, Gábor Csányi, A. P. Bart'ok, 2012, 1209.3140.

Comparing molecules and solids across structural and alchemical space.

Gábor Csányi, A. Bartók, M. Ceriotti, 2015, Physical chemistry chemical physics : PCCP.

Gaussian approximation potentials: A brief tutorial introduction

Gábor Csányi, A. Bartók, 2015, 1502.01366.

Computational Surface Chemistry of Tetrahedral Amorphous Carbon by Combining Machine Learning and Density Functional Theory

Volker L. Deringer, S. Elliott, T. Laurila, 2018, Chemistry of Materials.

Machine-Learning of Atomic-Scale Properties Based on Physical Principles

Michael J. Willatt, Gábor Csányi, M. Ceriotti, 2020 .

A machine learning based intramolecular potential for a flexible organic molecule.

Gábor Csányi, D. Cole, L. Mones, 2020, Faraday discussions.

Learning the electronic density of states in condensed matter

Gábor Csányi, M. Ceriotti, Chiheb Ben Mahmoud, 2020, 2006.11803.

Grand canonical approach to modeling hydrogen trapping at vacancies in α−Fe

Gábor Csányi, M. Finnis, E. R. M. Davidson, 2020 .

Machine learning interatomic potential developed for molecular simulations on thermal properties of β-Ga2O3.

Gábor Csányi, Yuanbin Liu, Jia-yue Yang, 2020, The Journal of chemical physics.

Regularised atomic body-ordered permutation-invariant polynomials for the construction of interatomic potentials

Cas van der Oord, Gábor Csányi, G. Dusson, 2019, Mach. Learn. Sci. Technol..

A general-purpose machine-learning force field for bulk and nanostructured phosphorus

Volker L. Deringer, Gábor Csányi, M. Caro, 2020, Nature Communications.

A Performance and Cost Assessment of Machine Learning Interatomic Potentials.

J. Behler, Z. Deng, Chi Chen, 2019, The journal of physical chemistry. A.

Machine-learned multi-system surrogate models for materials prediction

Conrad W. Rosenbrock, Tim Mueller, Matthias Rupp, 2018, npj Computational Materials.

Machine Learning Interatomic Potentials as Emerging Tools for Materials Science

Volker L. Deringer, Gábor Csányi, M. Caro, 2019, Advanced materials.

First-principles study of alkali-metal intercalation in disordered carbon anode materials

Jun Cheng, Volker L. Deringer, Gábor Csányi, 2019, Journal of Materials Chemistry A.

Machine Learning of Atomic-Scale Properties Based on Physical Principles

Michele Ceriotti, Michael J. Willatt, G'abor Cs'anyi, 2019, Handbook of Materials Modeling.

De novo exploration and self-guided learning of potential-energy surfaces

Noam Bernstein, Volker L. Deringer, Gábor Csányi, 2019, npj Computational Materials.

Machine Learning a General-Purpose Interatomic Potential for Silicon

Noam Bernstein, Gabor Csanyi, James Kermode, 2018, Physical Review X.

Development of a machine learning potential for graphene

Gábor Csányi, Dario Alfè, Angelos Michaelides, 2017, 1710.04187.

Gaussian approximation potential modeling of lithium intercalation in carbon nanostructures.

Volker L. Deringer, S. Elliott, Gábor Csányi, 2017, The Journal of chemical physics.

Comparison of permutationally invariant polynomials, neural networks, and Gaussian approximation potentials in representing water interactions through many-body expansions.

Jörg Behler, Michele Ceriotti, Gábor Csányi, 2018, The Journal of chemical physics.

Understanding the thermal properties of amorphous solids using machine-learning-based interatomic potentials

Volker L. Deringer, Gábor Csányi, Stephen R. Elliott, 2018 .

Realistic Atomistic Structure of Amorphous Silicon from Machine-Learning-Driven Molecular Dynamics.

Noam Bernstein, Volker L. Deringer, Gábor Csányi, 2018, The journal of physical chemistry letters.

Achieving DFT accuracy with a machine-learning interatomic potential: thermomechanics and defects in bcc ferromagnetic iron

Gabor Csanyi, Nicola Marzari, Daniele Dragoni, 2017, 1706.10229.

Volker L. Deringer, Gábor Csányi, Volker L Deringer, 2018, The journal of physical chemistry letters.

Gaussian Process States: A Data-Driven Representation of Quantum Many-Body Physics

George H. Booth, A. Glielmo, Gábor Csányi, 2020, Physical Review X.

Mapping Materials and Molecules.

Johannes T. Margraf, Volker L. Deringer, Gábor Csányi, 2020, Accounts of chemical research.

Surface diffusion: the low activation energy path for nanotube growth.

M. Payne, J. Robertson, Gábor Csányi, 2005, Physical review letters.

Structure of a large social network.

Gábor Csányi, B. Szendrői, Balázs Szendrői, 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

Structural transitions in dense disordered silicon from quantum-accurate ultra-large-scale simulations

Volker L. Deringer, S. Elliott, David Alan Drabold, 2019, 1912.07344.

Determining pressure-temperature phase diagrams of materials

M. Payne, Gábor Csányi, A. P. Bart'ok, 2015, 1503.03404.

Constant-pressure nested sampling with atomistic dynamics.

Gábor Csányi, N. Bernstein, R. Baldock, 2017, Physical review. E.

Many-Body Coarse-Grained Interactions Using Gaussian Approximation Potentials.

Gábor Csányi, S. T. John, Gábor Csányi, 2016, The journal of physical chemistry. B.

Data-Driven Learning of Total and Local Energies in Elemental Boron.

Volker L. Deringer, Gábor Csányi, Volker L Deringer, 2017, Physical review letters.

Reactivity of Amorphous Carbon Surfaces: Rationalizing the Role of Structural Motifs in Functionalization Using Machine Learning

Tomi Laurila, Volker L. Deringer, Gábor Csányi, 2018, Chemistry of materials : a publication of the American Chemical Society.

Accuracy and transferability of Gaussian approximation potential models for tungsten

Gábor Csányi, Albert P. Bartók, Gábor Csányi, 2014 .

Machine learning based interatomic potential for amorphous carbon

Volker L. Deringer, G'abor Cs'anyi, Gábor Csányi, 2016, 1611.03277.

Multi Facet Learning in Hilbert Spaces

R. Kondor, T. Jebara, S. Ahnert, 2005 .

Edge-functionalized and substitutionally doped graphene nanoribbons: Electronic and spin properties

Gábor Csányi, S. Piscanec, A. Ferrari, 2007, Physical Review B.

Role of f electrons in the Fermi surface of the heavy fermion superconductor beta-YbAlB4.

Z. Fisk, L. Balicas, Gábor Csányi, 2008, Physical review letters.

Fractal-small-world dichotomy in real-world networks.

Gábor Csányi, Balázs Szendroi, Gábor Csányi, 2004, Physical review. E, Statistical, nonlinear, and soft matter physics.

Thermodynamics of CuPt nanoalloys

Gábor Csányi, K. Rossi, F. Baletto, 2018, Scientific Reports.

Hybrid atomistic simulation methods for materials systems

Noam Bernstein, Gábor Csányi, James R. Kermode, 2009 .

Nested sampling for materials: the case of hard spheres.

Gábor Csányi, Albert P Bartók, Lívia B Pártay, 2012, Physical review. E, Statistical, nonlinear, and soft matter physics.

Scaling behaviour in discrete traffic models

Gábor Csányi, J. Kertész, Gábor Csányi, 1995 .

Polytypism in the ground state structure of the Lennard-Jonesium.

Gábor Csányi, A. Bartók, C. Pickard, 2017, Physical Chemistry, Chemical Physics - PCCP.

Multiscale modeling of defects in semiconductors : a novel molecular-dynamics scheme

M. Payne, Gábor Csányi, J. Kermode, 2007 .

Machine learning in chemical reaction space

Gábor Csányi, K. Reuter, J. T. Margraf, 2020, Nature Communications.

First-principles energetics of water: a many-body analysis

Gábor Csányi, A. P. Bart'ok, D. Alfé, 2013, 1303.0751.

Functional movements of the GABA type A receptor.

M. Payne, Gábor Csányi, C. Várnai, 2020, Physical chemistry chemical physics : PCCP.

Electron delocalization and dimerization in solid C59N doped C60 fullerene.

S. Garaj, Gábor Csányi, L. Forró, 2005 .

Comment on "Manifolds of quasi-constant SOAP and ACSF fingerprints and the resulting failure to machine learn four-body interactions" [J. Chem. Phys. 156, 034302 (2022)].

Michael J. Willatt, Gábor Csányi, S. Pozdnyakov, 2022, Journal of Chemical Physics.

An Experimentally Driven Automated Machine Learned lnter-Atomic Potential for a Refractory Oxide pdf

Chris J. Benmore, Ganesh Sivaraman, Leighanne Gallington, 2020, Physical review letters.

The Design Space of E(3)-Equivariant Atom-Centered Interatomic Potentials

R. Drautz, Gábor Csányi, B. Kozinsky, 2022, 2205.06643.

Exploiting molecular dynamics in Nested Sampling simulations of small peptides

David L. Wild, Robert J. N. Baldock, Gábor Csányi, 2016, Comput. Phys. Commun..

MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields

Gábor Csányi, C. Ortner, G. Simm, 2022, 2206.07697.

Nested sampling for physical scientists

Leah F. South, M. Habeck, D. Wales, 2022, Nature Reviews Methods Primers.

Nested sampling for physical scientists

Leah F. South, M. Habeck, D. Wales, 2022, Nature Reviews Methods Primers.

A Hybrid Machine Learning Approach for Structure Stability Prediction in Molecular Co-crystal Screenings

Johannes T. Margraf, Gábor Csányi, K. Reuter, 2022, Journal of chemical theory and computation.

Graphite intercalation compounds under pressure : A first-principles density functional theory study

R. Needs, Gábor Csányi, C. Pickard, 2007 .

First-principles energetics of water clusters and ice: a many-body analysis.

Gábor Csányi, A. Bartók, D. Alfé, 2013, The Journal of chemical physics.

Institute for Advanced Simulation DFT Embedding and Coarse Graining Techniques

M. Payne, Gábor Csányi, J. Kermode, 2007 .

Hybrid Quantum/Classical Modeling of Material Systems: The “Learn on the Fly” Molecular Dynamics Scheme

M. Payne, Gábor Csányi, J. Kermode, 2010 .

A machine learning based intramolecular potential for a flexible organic molecule.

Gábor Csányi, D. Cole, L. Mones, 2019, Faraday discussions.

New physics of the 30 ◦ partial dislocation in silicon revealed through ab initio calculation

T. Arias, Gábor Csányi, S. Ismail-Beigi, 2000, cond-mat/0011147.

General machine-learning surrogate models for materials prediction

Conrad W. Rosenbrock, M. Rupp, D. Wingate, 2018, 1809.09203.

Machine learning driven simulated deposition of carbon films: From low-density to diamondlike amorphous carbon

Volker L. Deringer, T. Laurila, Gábor Csányi, 2020, Physical Review B.

Machine-learning-driven simulated deposition of carbon ﬁlms: from low-density to diamond-like amorphous carbon

Volker L. Deringer, T. Laurila, Gábor Csányi, 2020 .

Atomistic fracture in bcc iron revealed by active learning of Gaussian approximation potential

Gábor Csányi, E. Giessen, Lei Zhang, 2022, npj Computational Materials.

Ranking the information content of distance measures

A. Laio, Gábor Csányi, Bing Cheng, 2021, PNAS nexus.

Metapopulation dynamics across gradients - the relation between colonization and extinction in shaping the range edge

B. Oborny, Gábor Csányi, G. Meszéna, 2009 .

Prostaglandins and Other Lipid Mediators

Gábor Csányi, T. Zalewska, M. Gajda, 2012 .

Accurate hyperfine couplings for C59N

T. Arias, Gábor Csányi, 2002, Chemical Physics Letters.

Hyperactive Learning (HAL) for Data-Driven Interatomic Potentials

Cas van der Oord, Gábor Csányi, C. Ortner, 2022 .

Tensor-reduced atomic density representations

James P. Darby, Gábor Csányi, G. Hart, 2022, ArXiv.

MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields

Gábor Csányi, C. Ortner, G. Simm, 2022, ArXiv.

A Combined Machine Learning and High-Energy X-ray Diffraction Approach to Understanding Liquid and Amorphous Metal Oxides

Ian T. Foster, Gábor Csányi, Á. Vázquez-Mayagoitia, 2022, Journal of the Physical Society of Japan.

Chemically active substitutional nitrogen impurity in carbon nanotubes.

M. Payne, Gábor Csányi, A. Nevidomskyy, 2003, Physical review letters.

Topical Review - Statistical and Nonlinear Physics Nested sampling for materials

Gábor Csányi, N. Bernstein, L. Pártay, 2021 .

On the role of long-range electrostatics in machine-learned interatomic potentials for complex battery materials

Johannes T. Margraf, Gábor Csányi, K. Reuter, 2021, ACS Applied Energy Materials.

Comment on "Manifolds of quasi-constant SOAP and ACSF fingerprints and the resulting failure to machine learn four-body interactions" [J. Chem. Phys. 156, 034302 (2022)].

Michael J. Willatt, Gábor Csányi, S. Pozdnyakov, 2022, Journal of Chemical Physics.

PARAMAGNETIC STRUCTURE OF THE SOLITON OF THE 30 PARTIAL DISLOCATION IN SILICON

T. Arias, Gábor Csányi, S. Ismail-Beigi, 1998, cond-mat/9805328.

Hyperactive learning for data-driven interatomic potentials

Cas van der Oord, Gábor Csányi, C. Ortner, 2022, npj computational materials.

The Design Space of E(3)-Equivariant Atom-Centered Interatomic Potentials

Simon L. Batzner, R. Drautz, Gábor Csányi, 2022, ArXiv.

Compressing local atomic neighbourhood descriptors

James P. Darby, Gábor Csányi, J. Kermode, 2021, npj Computational Materials.

Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic Systems.

Andrea Grisafi, Michele Ceriotti, Gábor Csányi, 2017, Physical review letters.

Structural Simplicity as a Constraint on the Structure of Amorphous Silicon

C. Grey, Gábor Csányi, A. P. Bart'ok, 2016, 1609.00668.

Tensor-reduced atomic density representations

James P. Darby, Gábor Csányi, G. Hart, 2022, Physical review letters.

Hydrogen induced fast-fracture

S. S. Shishvan, Gábor Csányi, V. Deshpande, 2020, Journal of the Mechanics and Physics of Solids.

Nanomechanical investigation of Mo6S9-x Ix nanowire bundles.

D. Vrbanic, D. Mihailovic, Gábor Csányi, 2007, Small.

Machine-learned interatomic potentials for alloys and alloy phase diagrams

Conrad W. Rosenbrock, Gus L. W. Hart, Noam Bernstein, 2019, npj Computational Materials.

Modeling Molecular Interactions in Water: From Pairwise to Many-Body Potential Energy Functions

Gábor Csányi, Kjartan Thor Wikfeldt, Valeria Molinero, 2016, Chemical reviews.

Data-efficient machine learning for molecular crystal structure prediction†

Johannes T. Margraf, Gábor Csányi, K. Reuter, 2021, Chemical science.

Viscous state effect on the activity of Fe nanocatalysts.

Stefano Curtarolo, Gabor Csányi, Gábor Csányi, 2010, ACS nano.

Reinforcement of single-walled carbon nanotube bundles by intertube bridging

W Benoit, G Csányi, Gábor Csányi, 2004, Nature materials.

The role of f-electrons at the Fermi surface of the heavy fermion superconductor β-YbAlB4

Z. Fisk, L. Balicas, Gábor Csányi, 2009 .

Discovering the building blocks of atomic systems using machine learning: application to grain boundaries

Eric R. Homer, Conrad W. Rosenbrock, Gus L. W. Hart, 2017, npj Computational Materials.

Effects of 3-aminopyridine-induced seizures on platelet eicosanoid synthesis.

M. Szente, Gábor Csányi, L. Vécsei, 2008, Pharmacological reports : PR.

Implications of the BATTERY 2030+ AI‐Assisted Toolkit on Future Low‐TRL Battery Discoveries and Chemistries

M. Berecibar, Gábor Csányi, A. Bhowmik, 2021, Advanced Energy Materials.

Physics-Inspired Structural Representations for Molecules and Materials.

Gábor Csányi, M. Ceriotti, A. P. Bart'ok, 2021, Chemical reviews.

Origins of structural and electronic transitions in disordered silicon

Volker L. Deringer, David Alan Drabold, Gábor Csányi, 2021, Nature.

Free Energies of Fe‐O‐Si Ternary Liquids at High Temperatures and Pressures: Implications for the Evolution of the Earth's Core Composition

Gábor Csányi, D. Alfé, Jin Liu, 2022, Geophysical Research Letters.

Performant implementation of the atomic cluster expansion (PACE) and application to copper and silicon

Cas van der Oord, R. Drautz, Aidan Thompson, 2021, npj Computational Materials.

Gaussian Process Regression for Materials and Molecules

Volker L. Deringer, Gábor Csányi, A. Bartók, 2021, Chemical reviews.

Machine learning unifies the modeling of materials and molecules

Noam Bernstein, James R Kermode, Michele Ceriotti, 2017, Science Advances.

Exploration, Sampling, And Reconstruction of Free Energy Surfaces with Gaussian Process Regression.

Noam Bernstein, Gábor Csányi, Letif Mones, 2016, Journal of chemical theory and computation.

Machine-learned interatomic potentials by active learning: amorphous and liquid hafnium dioxide

Gábor Csányi, Á. Vázquez-Mayagoitia, C. Benmore, 2020, npj Computational Materials.

The role of the interlayer state in the electronic structure of superconducting graphite intercalated compounds

P. Littlewood, Gábor Csányi, C. Pickard, 2005 .

Completeness of Atomic Structure Representations

Michael J. Willatt, Gábor Csányi, A. Bartók, 2023, ArXiv.

Atomic cluster expansion: Completeness, efficiency and stability

Cas van der Oord, R. Drautz, Gábor Csányi, 2019, J. Comput. Phys..

Towards an atomistic understanding of disordered carbon electrode materials

Yuchen Hu, Volker L. Deringer, Gábor Csányi, 2018, Chemical communications.

Nested sampling for physical scientists

Leah F. South, M. Habeck, D. Wales, 2022, Nature Reviews Methods Primers.

Hierarchical machine learning of potential energy surfaces.

Gábor Csányi, Alexey Dral, Pavlo O. Dral, 2020, The Journal of chemical physics.

Diffusive nested sampling

Brendon J. Brewer, Gábor Csányi, Lívia B. Pártay, 2009, Stat. Comput..

Machine learning potentials for extended systems: a perspective

Jörg Behler, Gábor Csányi, J. Behler, 2021, The European Physical Journal B.

Modeling the Phase-Change Memory Material, Ge2Sb2Te5, with a Machine-Learned Interatomic Potential.

Noam Bernstein, Volker L. Deringer, Gábor Csányi, 2018, The journal of physical chemistry. B.

Roadmap on multiscale materials modeling

Alexander L. Shluger, Vasily V. Bulatov, Alejandro Strachan, 2020, Modelling and Simulation in Materials Science and Engineering.

Partitioning of sulfur between solid and liquid iron under Earth’s core conditions: Constraints from atomistic simulations with machine learning potentials

Gábor Csányi, D. Alfé, Zhigang Zhang, 2020, Geochimica et Cosmochimica Acta.

Tensor product expansions for correlation in quantum many-body systems

T. Arias, Gábor Csányi, 1998, cond-mat/9805388.

Electronic properties of chemically modi.ed graphene ribbons

Gábor Csányi, S. Piscanec, A. Ferrari, 2008 .

Analyzing the errors of DFT approximations for compressed water systems.

Gábor Csányi, A. Bartók, D. Alfé, 2014, The Journal of chemical physics.

Machine-learning approach for one- and two-body corrections to density functional theory: Applications to molecular and condensed water

Frederick R. Manby, Gábor Csányi, M. J. Gillan, 2013 .

Improved tensor-product expansions for the two-particle density matrix

T. Arias, S. Goedecker, Gábor Csányi, 2001, cond-mat/0107536.

Low-speed fracture instabilities in a brittle crystal

P. Gumbsch, A. De Vita, M. Payne, 2008, Nature.

Expressive Programming for Computational Physics in Fortran 950

Noam Bernstein, Gábor Csányi, Alessandro De Vita, 2007 .

C6Yb and graphite: A comparative high-pressure transport study

Gábor Csányi, L. Forró, M. Ellerby, 2007 .

An accurate and transferable machine learning potential for carbon.

Volker L. Deringer, A. Michaelides, Gábor Csányi, 2020, The Journal of chemical physics.

Author Correction: The utility of Next Generation Sequencing for molecular diagnostics in Rett syndrome

N. Baena, G. Antiñolo, Gábor Csányi, 2017, Scientific Reports.

MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields

Gábor Csányi, C. Ortner, G. Simm, 2022, NeurIPS.

Local invertibility and sensitivity of atomic structure-feature mappings

Gábor Csányi, S. Pozdnyakov, M. Ceriotti, 2021, Open research Europe.

Incompleteness of Atomic Structure Representations.

Christoph Ortner, Michele Ceriotti, Sergey N. Pozdnyakov, 2020, Physical review letters.

Kernel charge equilibration: efficient and accurate prediction of molecular dipole moments with a machine-learning enhanced electron density model

Johannes T. Margraf, Gábor Csányi, K. Reuter, 2021, Mach. Learn. Sci. Technol..

Accurate Reaction Barriers for Catalytic Pathways: An Automatic Training Protocol for Machine Learning Force Fields

Gábor Csányi, Edvin Fako, L. Schaaf, 2023, 2301.09931.

Multilayer atomic cluster expansion for semilocal interactions

R. Drautz, Gábor Csányi, C. Ortner, 2022, Physical Review Research.

Linear Atomic Cluster Expansion Force Fields for Organic Molecules: Beyond RMSE

Cas van der Oord, Gábor Csányi, D. Cole, 2021, Journal of chemical theory and computation.

Linear Atomic Cluster Expansion Force Fields for Organic Molecules: beyond RMSE

Gábor Csányi, C. Ortner, D. P. Kovács, 2021 .

Linear Atomic Cluster Expansion Force Fields for Organic Molecules: Beyond RMSE

Cas van der Oord, Gábor Csányi, D. Cole, 2021, Journal of chemical theory and computation.

Combining phonon accuracy with high transferability in Gaussian approximation potential models.

Volker L. Deringer, G. Hautier, Gábor Csányi, 2020, The Journal of chemical physics.

Symmetry-Aware Actor-Critic for 3D Molecular Design pdf

Jos'e Miguel Hern'andez-Lobato, José Miguel Hernández-Lobato, Gregor N. C. Simm, 2020, ICLR.

Growth Mechanism and Origin of High sp^{3} Content in Tetrahedral Amorphous Carbon.

Volker L. Deringer, T. Laurila, Gábor Csányi, 2018, Physical review letters.

Extracting Crystal Chemistry from Amorphous Carbon Structures

Volker L. Deringer, Gábor Csányi, D. Proserpio, 2017, Chemphyschem : a European journal of chemical physics and physical chemistry.

Electronic structure of superconducting graphite intercalate compounds: The role of the interlayer state

P. Littlewood, Gábor Csányi, C. Pickard, 2005, cond-mat/0503569.

The adaptive buffered force QM/MM method in the CP2K and AMBER software packages

Andrew Jones, Noam Bernstein, Andreas W. Götz, 2014, J. Comput. Chem..

Superionic conductivity in the Li4C60 fulleride polymer.

Gábor Csányi, D. Pontiroli, M. Riccò, 2009, Physical review letters.

Dynamic catalyst restructuring during carbon nanotube growth.

Gábor Csányi, A. Ferrari, M. Moseler, 2010, ACS nano.

Atomic permutationally invariant polynomials for fitting molecular force fields

Christoph Ortner, Gábor Csányi, Geneviève Dusson, 2020, Mach. Learn. Sci. Technol..

Evaluating boundary dependent errors in QM/MM simulations.

Petr Kulhánek, István Simon, Monika Fuxreiter, 2009, The journal of physical chemistry. B.

Quantifying Chemical Structure and Machine‐Learned Atomic Energies in Amorphous and Liquid Silicon

Noam Bernstein, Volker L. Deringer, Gábor Csányi, 2018, Angewandte Chemie.

Gaussian approximation potential for amorphous Si : H

S. Goodnick, Gábor Csányi, Reza Vatan Meidanshahi, 2021, Physical Review Materials.

Machine learning force fields based on local parametrization of dispersion interactions: Application to the phase diagram of C60

T. Ala‐Nissila, Gábor Csányi, A. P. Bart'ok, 2021, Physical Review B.

Quantifying Chemical Structure and Atomic Energies in Amorphous Silicon Networks.

Noam Bernstein, Volker L. Deringer, Stephen R. Elliott, 2018, 1811.11069.

"Learn on the fly": a hybrid classical and quantum-mechanical molecular dynamics simulation.

M C Payne, Gabor Csányi, T Albaret, 2004, Physical review letters.

Linear Atomic Cluster Expansion Force Fields for Organic Molecules: beyond RMSE

Gábor Csányi, C. Ortner, D. P. Kovács, 2021 .

Evaluation of the MACE Force Field Architecture: from Medicinal Chemistry to Materials Science

Gábor Csányi, Ilyes Batatia, D. P. Kovács, 2023, 2305.14247.

Free Energy Surface Reconstruction from Umbrella Samples Using Gaussian Process Regression.

Noam Bernstein, Gábor Csányi, Thomas Stecher, 2013, Journal of chemical theory and computation.

Elastic modulus of multi-walled carbon nanotubes produced by catalytic chemical vapour deposition

J. Nagy, Gábor Csányi, F. Béguin, 2005 .

Bayesian inference of the spatial distributions of material properties

Andrea Vigliotti, Gábor Csányi, Vikram Deshpande, 2018, Journal of the Mechanics and Physics of Solids.

Isoflurane does not aggregate inside POPC bilayers at high pressure: Implications for pressure reversal of general anaesthesia

M. Payne, Gábor Csányi, M. ElGamacy, 2015 .

A universal preconditioner for simulating condensed phase materials.

Noam Bernstein, Christoph Ortner, Nicholas Gould, 2016, The Journal of chemical physics.

Preconditioners for the geometry optimisation and saddle point search of molecular systems

Christoph Ortner, Gábor Csányi, Letif Mones, 2018, Scientific Reports.

Modelling (100) hydrogen-induced platelets in silicon with a multi-scale molecular dynamics approach

Gábor Csányi, L. Ciacchi, G. Moras, 2007 .

A novel molecular dynamics approach to large semiconductor systems

M. Payne, Gábor Csányi, G. Moras, 2006 .

Dynamic Local Structure in Caesium Lead Iodide: Spatial Correlation and Transient Domains.

A. Walsh, Gábor Csányi, C. Sutton, 2023, Small.

Equation of State of Fluid Methane from First Principles with Machine Learning Potentials.

Gábor Csányi, D. Hohl, M. Veit, 2018, Journal of chemical theory and computation.

Many-Body Dispersion Correction Effects on Bulk and Surface Properties of Rutile and Anatase TiO2

Volker L. Deringer, Gábor Csányi, 2016 .

Data-driven learning and prediction of inorganic crystal structures.

Volker L. Deringer, Gábor Csányi, Volker L Deringer, 2018, Faraday discussions.

Topologically invariant reaction coordinates for simulating multistate chemical reactions.

Gábor Csányi, Letif Mones, Gábor Csányi, 2012, The journal of physical chemistry. B.

Screw dislocation structure and mobility in body centered cubic Fe predicted by a Gaussian Approximation Potential

William A. Curtin, Nicola Marzari, Daniele Dragoni, 2018, npj Computational Materials.

Tests of an adaptive QM/MM calculation on free energy profiles of chemical reactions in solution.

Noam Bernstein, Gábor Csányi, Letif Mones, 2013, The journal of physical chemistry. B.

UvA-DARE (Digital Academic Repository) Nested Transition Path Sampling

P. Bolhuis, Gábor Csányi, 2018 .

wfl Python Toolkit for Creating Machine Learning Interatomic Potentials and Related Atomistic Simulation Workflows

Gábor Csányi, K. Reuter, N. Bernstein, 2023, 2306.11421.

Evaluation of the MACE force field architecture: From medicinal chemistry to materials science.

Gábor Csányi, Ilyes Batatia, D. P. Kovács, 2023, The Journal of chemical physics.

Structural Dynamics Descriptors for Metal Halide Perovskites

Gábor Csányi, Zhenzhu Li, J. Klarbring, 2023, 2305.11617.

Approximation of Potential Energy Surfaces with Spherical Harmonics pdf

Markus Bachmayr, Gabor Csanyi, Genevieve Dusson, 2019, ArXiv.

Towards a la carte accuracy in large scale computer simulations

Gábor Csányi, Gábor Csányi, 2005 .

Nested sampling for materials

Gábor Csányi, N. Bernstein, L. Pártay, 2021, The European Physical Journal B.

Origins of structural and electronic transitions in disordered silicon

Volker L. Deringer, David Alan Drabold, Gábor Csányi, 2021, Nature.

Machine Learning Inter-Atomic Potentials Generation Driven by Active Learning: A Case Study for Amorphous and Liquid Hafnium dioxide pdf

Álvaro Vázquez-Mayagoitia, Marius Stan, Ganesh Sivaraman, 2019, ArXiv.

Publisher’s Note: On representing chemical environments [Phys. Rev. B 87 , 184115 (2013)]

Risi Kondor, Gábor Csányi, Albert P. Bartók, 2013 .

Communication: energy benchmarking with quantum Monte Carlo for water nano-droplets and bulk liquid water.

Gábor Csányi, A. Bartók, D. Alfé, 2013, The Journal of chemical physics.

Machine-Learning Based Potential For Iron: Plasticity and Phase Transition

Gábor Csányi, J. Maillet, C. Denoual, 2018 .

Development of a classical force field for the oxidized Si surface: application to hydrophilic wafer bonding.

Gábor Csányi, Daniel J Cole, Mike C Payne, 2007, The Journal of chemical physics.

Multiscale Simulation Methods in Molecular Sciences

Gábor Csányi, Mike C. Payne, James R. Kermode, 2009 .

Efficient, Accurate, and Transferable Machine Learning Potentials: Application to Dislocations and Cracks in Iron

Gábor Csányi, E. Giessen, F. Maresca, 2023 .

Strain rate sensitivity of the hydrogen embrittlement of ferritic steels

S. S. Shishvan, Gábor Csányi, V. S. Deshpande, 2023, Acta Materialia.

Performant implementation of the atomic cluster expansion.

Cas van der Oord, A. Thompson, R. Drautz, 2021 .

Predicting polarizabilities of silicon clusters using local chemical environments

Gábor Csányi, J. Lischner, Stefano Dal Forno, 2021, Mach. Learn. Sci. Technol..

wfl Python toolkit for creating machine learning interatomic potentials and related atomistic simulation workflows.

Gábor Csányi, K. Reuter, N. Bernstein, 2023, The Journal of chemical physics.

Electron delocalization and dimerization in solid C59N doped C60 fullerene.

S. Garaj, Gábor Csányi, L. Forró, 2005, Physical review letters.

Nested Transition Path Sampling.

Peter G. Bolhuis, Gábor Csányi, P. Bolhuis, 2018, Physical review letters.

Structural Dynamics Descriptors for Metal Halide Perovskites

Gábor Csányi, Zhenzhu Li, J. Klarbring, 2023, The journal of physical chemistry. C, Nanomaterials and interfaces.

Global analysis of energy landscapes for materials modeling: A test case for C60.

Gábor Csányi, J. W. Morgan, David J Wales, 2023, The Journal of chemical physics.

Completeness of Atomic Structure Representations

Michael J. Willatt, Gábor Csányi, A. Bartók, 2023, APL Machine Learning.

Extravasation of Blood and Blood Toxicity Drives Tubular Injury from RBC Trapping in Ischemic AKI

Gábor Csányi, J. Sullivan, B. Marshall, 2023, Function.

A Performance and Cost Assessment of Machine Learning Interatomic Potentials.

J. Behler, Z. Deng, Chi Chen, 2019, The journal of physical chemistry. A.

Efficient sampling of atomic configurational spaces.

Gábor Csányi, Albert P Bartók, Lívia B Pártay, 2009, The journal of physical chemistry. B.

Realistic Atomistic Structure of Amorphous Silicon from Machine-Learning-Driven Molecular Dynamics.

Volker L. Deringer, Lauren E. Marbella, C. Grey, 2018, The journal of physical chemistry letters.

Machine-learned acceleration for molecular dynamics in CASTEP.

Volker L. Deringer, Gábor Csányi, A. Bartók, 2023, The Journal of chemical physics.

Experimentally Driven Automated Machine-Learned Interatomic Potential for a Refractory Oxide.

Gábor Csányi, Á. Vázquez-Mayagoitia, C. Benmore, 2021, Physical review letters.

Constant-pressure nested sampling with atomistic dynamics.

Gábor Csányi, N. Bernstein, R. Baldock, 2017, Physical review. E.

First-principles spectroscopy of aqueous interfaces using machine-learned electronic and quantum nuclear effects.

A. Michaelides, Gábor Csányi, V. Kapil, 2023, Faraday discussions.

Machine learning force fields for molecular liquids: Ethylene Carbonate/Ethyl Methyl Carbonate binary solvent

C. Grey, Gábor Csányi, K. Hermansson, 2023, npj Computational Materials.

Accuracy and transferability of GAP models for tungsten

Gábor Csányi, A. P. Bart'ok, W. J. Szlachta, 2014, 1405.4370.

Comparison of permutationally invariant polynomials, neural networks, and Gaussian approximation potentials in representing water interactions through many-body expansions.

J. Behler, Gábor Csányi, M. Ceriotti, 2018, The Journal of chemical physics.

On the Completeness of Atomic Structure Representations

Michael J. Willatt, Gábor Csányi, A. Bartók, 2020, 2001.11696.

The role of the interlayer state in the electronic structure of superconducting graphite intercalated compounds

P. Littlewood, Gábor Csányi, C. Pickard, 2005, cond-mat/0503569.

Multiscale hybrid simulation methods for material systems

Mike C. Payne, Gábor Csányi, T Albaret, 2005 .

Polynomial epidemics and clustering in contact networks

Gábor Csányi, Balázs Szendroi, Gábor Csányi, 2004, Proceedings of the Royal Society of London. Series B: Biological Sciences.

Machine learning unifies the modeling of materials and molecules

Gábor Csányi, J. Kermode, A. Bartók, 2017, Science Advances.

Machine learning in chemical reaction space

Johannes T. Margraf, Gábor Csányi, K. Reuter, 2020, Nature Communications.

QM/MM simulation of liquid water with an adaptive quantum region.

Noam Bernstein, István Simon, Gábor Csányi, 2012, Physical chemistry chemical physics : PCCP.

ACEpotentials.jl: A Julia implementation of the atomic cluster expansion.

Cas van der Oord, Gábor Csányi, J. Kermode, 2023, The Journal of chemical physics.