Gábor Csányi

Publication Activity (10 Years)

Years Active: 2017-2023
Publications (10 Years): 19

Top Topics

Machine Learning

Angiotensin Converting Enzyme

Crystal Structure

Molecular Dynamics

Top Venues

Journal of chemical theory and computation

Chemical reviews

Nature communications

npj computational materials

This page only lists publications with an associated author ORCID identifier.

Publications

Elena Gelžinytė, Mario Öeren, Matthew D Segall, Gábor Csányi
Transferable Machine Learning Interatomic Potential for Bond Dissociation Energy Prediction of Drug-like Molecules. Journal of chemical theory and computation 20 (1) (2023)
Elena Gelžinytė, Mario Öeren, Matthew D Segall, Gábor Csányi
Transferable Machine Learning Interatomic Potential for Bond Dissociation Energy Prediction of Drug-like Molecules. Journal of chemical theory and computation 20 (1) (2023)
Cas van der Oord, Matthias Sachs, Dávid Péter Kovács, Christoph Ortner, Gábor Csányi
Hyperactive learning for data-driven interatomic potentials. npj computational materials 9 (1) (2023)
Sina Stocker, Hyunwook Jung, Gábor Csányi, C Franklin Goldsmith, Karsten Reuter, Johannes T Margraf
Estimating Free Energy Barriers for Heterogeneous Catalytic Reactions with Machine Learning Potentials and Umbrella Integration. Journal of chemical theory and computation (2023)
Venkat Kapil, Dávid Péter Kovács, Gábor Csányi, Angelos Michaelides
First-principles spectroscopy of aqueous interfaces using machine-learned electronic and quantum nuclear effects. Faraday discussions (2023)
Elena Gelžinytė, Mario Öeren, Matthew D Segall, Gábor Csányi
Transferable Machine Learning Interatomic Potential for Bond Dissociation Energy Prediction of Drug-like Molecules. Journal of chemical theory and computation (2023)
Elena Gelžinytė, Mario Öeren, Matthew D Segall, Gábor Csányi
Transferable Machine Learning Interatomic Potential for Bond Dissociation Energy Prediction of Drug-like Molecules. Journal of chemical theory and computation (2023)
Elena Gelžinytė, Mario Öeren, Matthew D Segall, Gábor Csányi
Transferable Machine Learning Interatomic Potential for Bond Dissociation Energy Prediction of Drug-like Molecules. Journal of chemical theory and computation 20 (1) (2023)
Cas van der Oord, Matthias Sachs, Dávid Péter Kovács, Christoph Ortner, Gábor Csányi
Hyperactive learning for data-driven interatomic potentials. npj computational materials 9 (1) (2023)
Xia Liang, Johan Klarbring, William J Baldwin, Zhenzhu Li, Gábor Csányi, Aron Walsh
Structural Dynamics Descriptors for Metal Halide Perovskites. The journal of physical chemistry. C, Nanomaterials and interfaces 127 (38) (2023)
Dávid Péter Kovács, Cas van der Oord, Jiri Kucera, Alice E A Allen, Daniel J Cole, Christoph Ortner, Gábor Csányi
Linear Atomic Cluster Expansion Force Fields for Organic Molecules: Beyond RMSE. Journal of chemical theory and computation (2021)
Volker L Deringer, Albert P Bartók, Noam Bernstein, David M Wilkins, Michele Ceriotti, Gábor Csányi
Gaussian Process Regression for Materials and Molecules. Chemical reviews 121 (16) (2021)
Simon Wengert, Gábor Csányi, Karsten Reuter, Johannes T Margraf
Data-efficient machine learning for molecular crystal structure prediction. Chemical science 12 (12) (2021)
Volker L Deringer, Noam Bernstein, Gábor Csányi, Chiheb Ben Mahmoud, Michele Ceriotti, Mark Wilson, David A Drabold, Stephen R Elliott
Origins of structural and electronic transitions in disordered silicon. Nature 589 (7840) (2021)
Félix Musil, Andrea Grisafi, Albert P Bartók, Christoph Ortner, Gábor Csányi, Michele Ceriotti
Physics-Inspired Structural Representations for Molecules and Materials. Chemical reviews 121 (16) (2021)
Sina Stocker, Gábor Csányi, Karsten Reuter, Johannes T Margraf
Machine learning in chemical reaction space. Nature communications 11 (1) (2020)
Volker L Deringer, Miguel A Caro, Gábor Csányi
A general-purpose machine-learning force field for bulk and nanostructured phosphorus. Nature communications 11 (1) (2020)
Csilla Varnai, Benedict W J Irwin, M C Payne, Gábor Csányi, P-L Chau
Functional movements of the GABA type A receptor. Physical chemistry chemical physics : PCCP 22 (28) (2020)
Albert P Bartók, Sandip De, Carl Poelking, Noam Bernstein, James R Kermode, Gábor Csányi, Michele Ceriotti
Machine learning unifies the modeling of materials and molecules. Science advances 3 (12) (2017)