Polarisable multipolar electrostatics from the machine learning method Kriging: An application to alanine

Matthew J L Mills; Paul L A Popelier

doi:10.1007/s00214-012-1137-7

Polarisable multipolar electrostatics from the machine learning method Kriging: An application to alanine

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Abstract

We present a polarisable multipolar interatomic electrostatic potential energy function for force fields and describe its application to the pilot molecule MeNH-Ala-COMe (AlaD). The total electrostatic energy associated with 1, 4 and higher interactions is partitioned into atomic contributions by application of quantum chemical topology (QCT). The exact atom-atom interaction is expressed in terms of atomic multipole moments. The machine learning method Kriging is used to model the dependence of these multipole moments on the conformation of the entire molecule. The resulting models are able to predict the QCT-partitioned multipole moments for arbitrary chemically relevant molecular geometries. The interaction energies between atoms are predicted for these geometries and compared to their true values. The computational expense of the procedure is compared to that of the point charge formalism. © 2012 Springer-Verlag.

Original language	English
Pages (from-to)	1-16
Number of pages	15
Journal	Theoretical Chemistry Accounts
Volume	131
Issue number	3
DOIs	https://doi.org/10.1007/s00214-012-1137-7
Publication status	Published - Mar 2012

Keywords

Atoms in molecules
Force field
Machine learning
Multipole moment
Polarisation
Quantum chemical topology

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10.1007/s00214-012-1137-7

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title = "Polarisable multipolar electrostatics from the machine learning method Kriging: An application to alanine",

abstract = "We present a polarisable multipolar interatomic electrostatic potential energy function for force fields and describe its application to the pilot molecule MeNH-Ala-COMe (AlaD). The total electrostatic energy associated with 1, 4 and higher interactions is partitioned into atomic contributions by application of quantum chemical topology (QCT). The exact atom-atom interaction is expressed in terms of atomic multipole moments. The machine learning method Kriging is used to model the dependence of these multipole moments on the conformation of the entire molecule. The resulting models are able to predict the QCT-partitioned multipole moments for arbitrary chemically relevant molecular geometries. The interaction energies between atoms are predicted for these geometries and compared to their true values. The computational expense of the procedure is compared to that of the point charge formalism. {\textcopyright} 2012 Springer-Verlag.",

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T1 - Polarisable multipolar electrostatics from the machine learning method Kriging: An application to alanine

AU - Mills, Matthew J L

AU - Popelier, Paul L A

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N2 - We present a polarisable multipolar interatomic electrostatic potential energy function for force fields and describe its application to the pilot molecule MeNH-Ala-COMe (AlaD). The total electrostatic energy associated with 1, 4 and higher interactions is partitioned into atomic contributions by application of quantum chemical topology (QCT). The exact atom-atom interaction is expressed in terms of atomic multipole moments. The machine learning method Kriging is used to model the dependence of these multipole moments on the conformation of the entire molecule. The resulting models are able to predict the QCT-partitioned multipole moments for arbitrary chemically relevant molecular geometries. The interaction energies between atoms are predicted for these geometries and compared to their true values. The computational expense of the procedure is compared to that of the point charge formalism. © 2012 Springer-Verlag.

AB - We present a polarisable multipolar interatomic electrostatic potential energy function for force fields and describe its application to the pilot molecule MeNH-Ala-COMe (AlaD). The total electrostatic energy associated with 1, 4 and higher interactions is partitioned into atomic contributions by application of quantum chemical topology (QCT). The exact atom-atom interaction is expressed in terms of atomic multipole moments. The machine learning method Kriging is used to model the dependence of these multipole moments on the conformation of the entire molecule. The resulting models are able to predict the QCT-partitioned multipole moments for arbitrary chemically relevant molecular geometries. The interaction energies between atoms are predicted for these geometries and compared to their true values. The computational expense of the procedure is compared to that of the point charge formalism. © 2012 Springer-Verlag.

KW - Atoms in molecules

KW - Force field

KW - Machine learning

KW - Multipole moment

KW - Polarisation

KW - Quantum chemical topology

U2 - 10.1007/s00214-012-1137-7

DO - 10.1007/s00214-012-1137-7

M3 - Article

VL - 131

SP - 1

EP - 16

JO - Theoretical Chemistry Accounts

JF - Theoretical Chemistry Accounts

IS - 3

ER -

Polarisable multipolar electrostatics from the machine learning method Kriging: An application to alanine

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Keywords

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