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Gaussian Process-Based Refinement of Dispersion Corrections.

Jonny Proppe1,2, Stefan Gugler2, Markus Reiher2

  • 1Department of Chemistry , and Department of Computer Science , University of Toronto , Toronto , Ontario M5S , Canada.

Journal of Chemical Theory and Computation
|October 12, 2019
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Summary
This summary is machine-generated.

Gaussian process regression (D3-GP) refines D3-type dispersion corrections by learning from high-level calculations. This self-improving model efficiently selects optimal training data, enhancing accuracy for molecular systems.

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Area of Science:

  • Computational Chemistry
  • Quantum Chemistry
  • Method Development

Background:

  • Systematic errors in D3-type dispersion corrections limit accuracy.
  • Accurate dispersion corrections are crucial for predicting molecular interactions.
  • Existing methods require careful parameterization and extensive reference data.

Purpose of the Study:

  • To develop a statistically improved dispersion correction model (D3-GP) using Gaussian process (GP) regression.
  • To address systematic errors in D3-type dispersion corrections.
  • To create an automated, self-improving workflow for dispersion correction refinement.

Main Methods:

  • Employed Gaussian process (GP) regression trained on interaction energies from PBE-D3(BJ)/ma-def2-QZVPP and DLPNO-CCSD(T)/CBS calculations.
  • Engineered vectorial representations (histD3(BJ) and eigD3(BJ)) from atom-pairwise D3(BJ) interaction terms.
  • Utilized batchwise variance-based sampling (BVS) for automated, efficient selection of training data.

Main Results:

  • A reparametrization of D3(BJ) yielded parameters a1=0, s8=0, and a2=5.6841 bohr, outperforming previous sets.
  • The D3-GP model achieved dispersion corrections comparable to the original D3 approach, with systematic improvements.
  • Variance-based sampling effectively identified minimal training subsets to meet accuracy thresholds.

Conclusions:

  • The D3-GP model offers a self-improving approach to accurate, system-focused dispersion corrections.
  • Batchwise variance-based sampling (BVS) significantly enhances the efficiency of the D3-GP workflow.
  • This black-box implementation allows automatic adaptation to new molecular systems and reference data.