Electrostatic Forces: Formulas for the First Derivatives of a Polarizable, Anisotropic Electrostatic Potential Energy Function Based on Machine Learning

Matthew J L Mills; Paul L A Popelier

doi:10.1021/ct500565g

Electrostatic Forces: Formulas for the First Derivatives of a Polarizable, Anisotropic Electrostatic Potential Energy Function Based on Machine Learning

Matthew J L Mills
, Paul L A Popelier

Research output: Contribution to journal › Article › peer-review

Original language	English
Pages (from-to)	3840-3856
Number of pages	17
Journal	Journal of Chemical Theory and Computation
Volume	10
Issue number	9
DOIs	https://doi.org/10.1021/ct500565g
Publication status	Published - 2014

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10.1021/ct500565g

Cite this

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title = "Electrostatic Forces: Formulas for the First Derivatives of a Polarizable, Anisotropic Electrostatic Potential Energy Function Based on Machine Learning",

author = "Mills, \{Matthew J L\} and Popelier, \{Paul L A\}",

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