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Benchmarking a foundation potential against quantum chemistry methods for predicting molecular redox potentials

Yicheng Chen, Lixue Cheng, Yan Jing, Peichen Zhong

TL;DR

This work benchmarks the MACE-OMol foundation potential against a hierarchy of DFT methods for predicting experimental redox potentials of ET and PCET reactions, addressing the computational bottleneck in high-throughput screening. By employing a Born-Haber cycle with an implicit solvent (SMD) and a decomposition of $G_{(sol)}$ into gas-phase energy, gas-phase thermochemistry, and solvation contributions, the study assesses MACE-OMol's ability to predict redox potentials across diverse chemical spaces. PCET predictions are notably accurate with MACE-OMol, rivaling target DFT after single-point correction, while ET predictions, especially for multi-electron transfers involving reactive ions, show clear out-of-distribution weaknesses. The authors propose a pragmatic hybrid workflow that leverages FP efficiency for geometry and thermochemistry, supplemented by targeted DFT single-point refinement and solvation corrections, enabling robust, scalable screening for sustainable electrochemical applications.

Abstract

Computational high-throughput virtual screening is essential for identifying redox-active molecules for sustainable applications such as electrochemical carbon capture. A primary challenge in this approach is the high computational cost associated with accurate quantum chemistry calculations. Machine learning foundation potentials (FPs) trained on extensive density functional theory (DFT) calculations offer a computationally efficient alternative. Here, we benchmark the MACE-OMol-0 FP against a hierarchy of DFT functionals for predicting experimental molecular redox potentials for both electron transfer (ET) and proton-coupled electron transfer (PCET) reactions. We find that MACE-OMol achieves exceptional accuracy for PCET processes, rivaling its target DFT method. However, its performance is diminished for ET reactions, particularly for multi-electron transfers involving reactive ions that are underrepresented in the OMol25 training data, revealing a key out-of-distribution limitation. To overcome this, we propose an optimal hybrid workflow that uses the FP for efficient geometry optimization and thermochemical analysis, followed by a crucial single-point DFT energy refinement and an implicit solvation correction. This pragmatic approach provides a robust and scalable strategy for accelerating high-throughput virtual screening in sustainable chemistry.

Benchmarking a foundation potential against quantum chemistry methods for predicting molecular redox potentials

TL;DR

This work benchmarks the MACE-OMol foundation potential against a hierarchy of DFT methods for predicting experimental redox potentials of ET and PCET reactions, addressing the computational bottleneck in high-throughput screening. By employing a Born-Haber cycle with an implicit solvent (SMD) and a decomposition of into gas-phase energy, gas-phase thermochemistry, and solvation contributions, the study assesses MACE-OMol's ability to predict redox potentials across diverse chemical spaces. PCET predictions are notably accurate with MACE-OMol, rivaling target DFT after single-point correction, while ET predictions, especially for multi-electron transfers involving reactive ions, show clear out-of-distribution weaknesses. The authors propose a pragmatic hybrid workflow that leverages FP efficiency for geometry and thermochemistry, supplemented by targeted DFT single-point refinement and solvation corrections, enabling robust, scalable screening for sustainable electrochemical applications.

Abstract

Computational high-throughput virtual screening is essential for identifying redox-active molecules for sustainable applications such as electrochemical carbon capture. A primary challenge in this approach is the high computational cost associated with accurate quantum chemistry calculations. Machine learning foundation potentials (FPs) trained on extensive density functional theory (DFT) calculations offer a computationally efficient alternative. Here, we benchmark the MACE-OMol-0 FP against a hierarchy of DFT functionals for predicting experimental molecular redox potentials for both electron transfer (ET) and proton-coupled electron transfer (PCET) reactions. We find that MACE-OMol achieves exceptional accuracy for PCET processes, rivaling its target DFT method. However, its performance is diminished for ET reactions, particularly for multi-electron transfers involving reactive ions that are underrepresented in the OMol25 training data, revealing a key out-of-distribution limitation. To overcome this, we propose an optimal hybrid workflow that uses the FP for efficient geometry optimization and thermochemical analysis, followed by a crucial single-point DFT energy refinement and an implicit solvation correction. This pragmatic approach provides a robust and scalable strategy for accelerating high-throughput virtual screening in sustainable chemistry.

Paper Structure

This paper contains 7 sections, 4 equations, 2 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Methods
  3. Results
  4. ET reactions of Lewis bases
  5. PCET reactions of quinones with polar groups
  6. ET/PCET of quinone-based molecules
  7. Discussion

Figures (2)

  • Figure 1: Overview of the computational workflow. (a) Key steps for calculating free energy, including conformer search, geometry optimization, vibrational frequency analysis, and single-point energy correction. (b) The Born-Haber cycle used to incorporate the solvation free energy ($\delta G_\text{solv}$) via the SMD implicit solvent model. (c) The three experimental datasets used for benchmarking, covering electron transfer (ET) and proton-coupled electron transfer (PCET) reactions.
  • Figure 2: Discrepancy between Hessian matrices predicted by MACE-OMol and the reference $\omega$B97M-V/def2-TZVPD method. The panels show the element-wise difference for (a) BNSN- (ET 1e-), (b) BNSN^2- (ET 2e-), (c) H2AQDCl18 with PCET, and (d) $\text{AQDCl18}^{\cdot -}$ (ET 1e-).