Behler, Jörg

Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces

Behler, J., & Parrinello, M. (2007) · Physical Review Letters

Machine Learning in Materials ScienceAdvanced Chemical Physics Studies

Cited by 5,010

Performance and Cost Assessment of Machine Learning Interatomic Potentials

Zuo, Y., Chen, C., Li, X., Deng, Z., Chen, Y., Behler, J., Csányi, G., Shapeev, A. V., Thompson, A. P., Wood, M. A., et al. (2020) · The Journal of Physical Chemistry A

Machine Learning in Materials ScienceAdvanced Chemical Physics Studies

Cited by 968

Constructing high‐dimensional neural network potentials: A tutorial review

Behler, J. (2015) · International Journal of Quantum Chemistry

Machine Learning in Materials ScienceAdvanced Chemical Physics Studies

Cited by 884

Neural network potential-energy surfaces in chemistry: a tool for large-scale simulations

Behler, J. (2011) · Physical Chemistry Chemical Physics

Machine Learning in Materials ScienceAdvanced Chemical Physics Studies

Cited by 781

Machine learning molecular dynamics for the simulation of infrared spectra

Gastegger, M., Behler, J., & Marquetand, P. (2017) · Chemical Science

Machine Learning in Materials ScienceSpectroscopy and Quantum Chemical Studies

Cited by 556

Automatic selection of atomic fingerprints and reference configurations for machine-learning potentials

Imbalzano, G., Anelli, A., Giofré, D., Klees, S., Behler, J., & Ceriotti, M. (2018) · The Journal of Chemical Physics

Machine Learning in Materials ScienceAdvanced Chemical Physics Studies

Cited by 330

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

Nguyen, T. T., Székely, E., Imbalzano, G., Behler, J., Csányi, G., Ceriotti, M., Götz, A. W., & Paesani, F. (2018) · The Journal of Chemical Physics

Advanced Chemical Physics StudiesMachine Learning in Materials Science

Cited by 171

Predicting oxidation and spin states by high-dimensional neural networks: Applications to lithium manganese oxide spinels

Eckhoff, M., Lausch, K. N., Blöchl, P. E., & Behler, J. (2020) · The Journal of Chemical Physics

Machine Learning in Materials ScienceAdvancements in Battery Materials

Cited by 45