Gábor Csányi

发表

Gaussian Approximation Potentials for iron from extended first-principles database (Data Download)

Daniele Dragoni, Tom Daff, Gábor Csányi, 2017 .

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.

Machine learning properties of materials and molecules with entropy-regularized kernels

Michele Ceriotti, Gábor Csányi, Albert P. Bartók, 2017 .

Efficient sampling of atomic configurational spaces.

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

Atomic permutationally invariant polynomials for fitting molecular force fields

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

Expressive Programming for Computational Physics in Fortran 950

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

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.

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

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

Towards a la carte accuracy in large scale computer simulations

Gábor Csányi, Gábor Csányi, 2005 .

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..

Machine learning unifies the modeling of materials and molecules

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

A universal preconditioner for simulating condensed phase materials.

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

Data-driven learning and prediction of inorganic crystal structures.

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

Nested Transition Path Sampling.

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

Simultaneous structure exploration and machine-learned potential fitting

Noam Bernstein, Volker L. Deringer, Gábor Csányi, 2019 .

Research data supporting "Data-driven learning and prediction of inorganic crystal structures"

Volker L. Deringer, Gábor Csányi, Chris J. Pickard, 2018 .

Exploiting molecular dynamics in Nested Sampling simulations of small peptides

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

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

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

Hierarchical machine learning of potential energy surfaces.

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

Searching for the Holy Grail: Fast and accurate force fields

Gábor Csányi, 2018 .

Embedding and locality in quantum mechanics

Gábor Csányi, 2005 .

Phase diagrams of complex systems with nested sampling

Noam Bernstein, Gábor Csányi, K. Michael Salerno, 2018 .

Diffusive nested sampling

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

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

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

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

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

Erratum: Gaussian approximation potentials: A brief tutorial introduction (International Journal of Quantum Chemistry (2015) 115 (1051-1057))

Gábor Csányi, Albert P. Bartók, 2016 .

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.

Machine learning potentials for extended systems: a perspective

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

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 .

Research data supporting "Determining pressure-temperature phase diagrams of materials"

Robert John Nicholas Baldock, Gábor Csányi, Mike C. Payne, 2016 .

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

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

Machine-learned multi-system surrogate models for materials prediction

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

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.

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.

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

Topologically invariant reaction coordinates for simulating multistate chemical reactions.

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

Towards an atomistic understanding of disordered carbon electrode materials

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

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.

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

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

Accuracy and transferability of Gaussian approximation potential models for tungsten

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

On representing chemical environments (vol 87, 184115, 2013)

Risi Kondor, Gábor Csányi, Albert P. Bartók, 2017 .

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

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

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

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

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.

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.

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

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

Evaluating boundary dependent errors in QM/MM simulations.

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

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

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

Reactive Many-Body Expansion for a Protonated Water Cluster.

Gábor Csányi, Peter Pinski, 2014, Journal of chemical theory and computation.

Machine-learned interatomic potentials for alloys and alloy phase diagrams

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

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.

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.

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

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

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.

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

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

Development of a machine learning potential for graphene

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

Hybrid atomistic simulation methods for materials systems

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

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

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

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.

Multiscale Simulation Methods in Molecular Sciences

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

Scaling behaviour in discrete traffic models

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

Roadmap on multiscale materials modeling

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

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.