A Performance and Cost Assessment of Machine Learning Interatomic Potentials.

@article{Zuo2020APA,
  title={A Performance and Cost Assessment of Machine Learning Interatomic Potentials.},
  author={Yunxing Zuo and Chi Chen and Xiang-Guo Li and Zhi Chang Deng and Yiming Chen and J{\"o}rg Behler and G{\'a}bor Cs{\'a}nyi and Alexander V. Shapeev and Aidan P. Thompson and Mitchell A. Wood and Shyue Ping Ong},
  journal={The journal of physical chemistry. A},
  year={2020}
}
Machine learning of the quantitative relationship between local environment descriptors and the potential energy surface of a system of atoms has emerged as a new frontier in the development of interatomic potentials (IAPs). Here, we present a comprehensive evaluation of ML-IAPs based on four local environment descriptors --- atom-centered symmetry functions (ACSF), smooth overlap of atomic positions (SOAP), the Spectral Neighbor Analysis Potential (SNAP) bispectrum components, and moment… 

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