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

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

Access to Document

10.1021/ct500565g

Cite this

@article{c9d4dfd9ced04b219a0ec627984ae04b,

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}",

note = "Times Cited: 0",

year = "2014",

doi = "10.1021/ct500565g",

language = "English",

volume = "10",

pages = "3840--3856",

journal = "Journal of Chemical Theory and Computation",

issn = "1549-9626",

publisher = "American Chemical Society",

number = "9",

}

TY - JOUR

T1 - Electrostatic Forces: Formulas for the First Derivatives of a Polarizable, Anisotropic Electrostatic Potential Energy Function Based on Machine Learning

AU - Mills, Matthew J L

AU - Popelier, Paul L A

N1 - Times Cited: 0

PY - 2014

Y1 - 2014

U2 - 10.1021/ct500565g

DO - 10.1021/ct500565g

M3 - Article

SN - 1549-9626

VL - 10

SP - 3840

EP - 3856

JO - Journal of Chemical Theory and Computation

JF - Journal of Chemical Theory and Computation

IS - 9

ER -