Machine Learning of Dynamic Electron Correlation Energies from Topological Atoms.

James L Mcdonagh,Paul L A Popelier,Arnaldo F Silva,Mark A Vincent

doi:10.1021/acs.jctc.7b01157

James L Mcdonagh, Paul L A Popelier + Show 2 more

Open Access

https://doi.org/10.1021/acs.jctc.7b01157

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Abstract

We present an innovative method for predicting the dynamic electron correlation energy of an atom or a bond in a molecule utilizing topological atoms. Our approach uses the machine learning method Kriging (Gaussian Process Regression with a non-zero mean function) to predict these dynamic electron correlation energy contributions. The true energy values are calculated by partitioning the MP2 two-particle density-matrix via the Interacting Quantum Atoms (IQA) procedure. To our knowledge, this is the first time such energies have been predicted by a machine learning technique. We present here three important proof-of-concept cases: the water monomer, the water dimer, and the van der Waals complex H2···He. These cases represent the final step toward the design of a full IQA potential for molecular simulation. This final piece will enable us to consider situations in which dispersion is the dominant intermolecular interaction. The results from these examples suggest a new method by which dispersion potentials for molecular simulation can be generated.

Highlights

Dynamic electron correlation is a quantum mechanical phenomenon with profound ramifications for intermolecular interactions.[1−3] Typical quantum chemical methods commonly employed, such as DFT, generally do not completely include many of these correlation effects, and in some cases they are not included at all
In the current article we present the final energy contribution needed in order to complete the full description of the FFLUX force field for a chemical system’s dynamics: here we show how the MP2 correlation energy can be partitioned and successfully predicted by Machine learning (ML) methods
A training set of 40 geometries was created for the construction of Kriging models, which were tested on 60 geometries

Summary

Introduction

Dynamic electron correlation is a quantum mechanical phenomenon with profound ramifications for intermolecular interactions.[1−3] Typical quantum chemical methods commonly employed, such as DFT, generally do not completely include many of these correlation effects, and in some cases they are not included at all. A plethora of methods exists to obtain Electron Correlation Energies (ECE) These methods range from empirical potentials in simulation methods[4−12] to a posteriori corrections in DFT or ab initio wave function methods.[13−18] In addition, more recently, many-body correction terms have been employed to supplement DFT.[16,19]. These methods have been applied to a wide variety of important chemical systems including the solid state,[20−22] biological materials,[23] and more recently nanostructures.[24−26] Recent work has seen much improvement in methods[16,27] to account for ECE. Electron correlation is proportionally a small contribution energetically, it is ubiquitous and has been shown to be responsible for macroscopic phenomena, such as an explanation of the gecko’s ability to adhere to a variety of surfaces.[28]

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