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Factor analysis of error in oxidation potential calculation: A machine learning study.

Yuki Kanamaru1, Toru Matsui1

  • 1Department of Chemistry, Graduate School of Pure and Applied Science, University of Tsukuba, Tsukuba, Japan.

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|June 28, 2022
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Summary
This summary is machine-generated.

The conductor-like polarizable continuum model (C-PCM) overestimates oxidation potential in charged systems. Machine learning identified molecular size as a key factor, enabling corrections that significantly reduce calculation errors.

Keywords:
B3LYPC-PCMG3B3machine learningredox potential

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Area of Science:

  • Computational Chemistry
  • Quantum Chemistry
  • Physical Chemistry

Background:

  • The conductor-like polarizable continuum model (C-PCM) is a widely used, cost-effective solvation model.
  • C-PCM struggles to accurately capture solute-solvent interactions, particularly for charged systems, leading to significant errors.
  • Previous studies indicate that errors in C-PCM calculations are often linked to solvation energy inaccuracies in charged species.

Purpose of the Study:

  • To investigate the systematic errors in oxidation potential calculations using the G3B3/C-PCM method.
  • To identify the underlying causes of these errors, specifically focusing on the influence of solute molecular size.
  • To develop and validate a correction strategy to improve the accuracy of G3B3/C-PCM for charged systems.

Main Methods:

  • Employed machine learning techniques to analyze the correlation between solute molecular size and calculation errors.
  • Utilized the G3B3 composite method in conjunction with the C-PCM solvation model.
  • Developed a correction method incorporating solute substructure information to refine C-PCM predictions.

Main Results:

  • Machine learning revealed a strong correlation between solute molecular size and the systematic error in G3B3/C-PCM oxidation potential calculations.
  • The G3B3/C-PCM method overestimated Gibbs oxidation energy by an average of 6.94 kcal/mol.
  • The developed correction strategy, based on substructure information, reduced the error to 2.27–3.2 kcal/mol (a 32–40% improvement).

Conclusions:

  • The observed overestimation in oxidation potential by G3B3/C-PCM is primarily attributed to inaccuracies in solvation energy for charged solutes.
  • Incorporating effects related to molecular size in charged systems is crucial for improving the C-PCM.
  • The proposed correction method demonstrates a significant enhancement in the accuracy of oxidation potential calculations for charged species.