Bartók, Albert P.

Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons

Bartók, A. P., Payne, M. C., Kondor, R., & Csányi, G. (2010) · Physical Review Letters

Machine Learning in Materials ScienceAdvanced Chemical Physics Studies

Cited by 3,242

On representing chemical environments

Bartók, A. P., Kondor, R., & Csányi, G. (2013) · Physical Review B

Machine Learning in Materials ScienceAdvanced Chemical Physics Studies

Cited by 2,572

Comparing molecules and solids across structural and alchemical space

De, S., Bartók, A. P., Csányi, G., & Ceriotti, M. (2016) · Physical Chemistry Chemical Physics

Machine Learning in Materials ScienceAdvanced Chemical Physics Studies

Cited by 778

Machine learning unifies the modeling of materials and molecules

Bartók, A. P., De, S., Poelking, C., Bernstein, N., Kermode, J. R., Csányi, G., & Ceriotti, M. (2017) · Science Advances

Machine Learning in Materials ScienceAdvanced Chemical Physics Studies

Cited by 764

Gaussian approximation potentials: A brief tutorial introduction

Bartók, A. P., & Csányi, G. (2015) · International Journal of Quantum Chemistry

Machine Learning in Materials ScienceAdvanced Chemical Physics Studies

Cited by 617

Combining phonon accuracy with high transferability in Gaussian approximation potential models

George, J., Hautier, G., Bartók, A. P., Csányi, G., & Deringer, V. L. (2020) · The Journal of Chemical Physics

Machine Learning in Materials ScienceAdvanced Chemical Physics Studies

Cited by 44