# Learning Potential Energy Landscapes using Graph Kernels

@article{Ferr2016LearningPE, title={Learning Potential Energy Landscapes using Graph Kernels}, author={Gr{\'e}goire Ferr{\'e} and Terry S. Haut and Kipton Marcos Barros}, journal={ArXiv}, year={2016}, volume={abs/1612.00193} }

Recent machine learning methods make it possible to model potential energy of atomic configurations with chemical-level accuracy (as calculated from ab-initio calculations) and at speeds suitable for molecular dynamics simulation. Best performance is achieved when the known physical constraints are encoded in the machine learning models. For example, the atomic energy is invariant under global translations and rotations; it is also invariant to permutations of same-species atoms. Although…

## One Citation

Towards exact molecular dynamics simulations with machine-learned force fields

- ChemistryNature Communications
- 2018

A flexible machine-learning force-field with high-level accuracy for molecular dynamics simulations is developed, for flexible molecules with up to a few dozen atoms and insights into the dynamical behavior of these molecules are provided.

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