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September 10, 2025

Benchmarking OMol25-Trained Models on Experimental Reduction-Potential and Electron-Affinity Data

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Authors

SVSawyer VanZanten CWCorin WagenHarvard University Press

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Implication

Analysis demonstrates predictive performance of neural network potentials for reduction-potential and electron-affinity, suggesting potential advantages over DFT and SQM methods.

Key Points

OMol25-trained neural network potentials show similar accuracy to traditional methods for predicting electron-affinity.
Evaluation reveals that these models outperform low-cost DFT and semiempirical methods for charge-related properties.
Findings indicate neural network potentials can effectively predict chemical properties despite lacking explicit physics consideration.
OMol25-trained models tend to more accurately predict charge-related properties for organometallic species than for main-group species.

Cite This Study

VanZanten et al. (2025) studied this question.

synapsesocial.com/papers/68c1956b9b7b07f3a0619ae0 https://doi.org/10.26434/chemrxiv-2025-3stpx

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Implication

Analysis demonstrates predictive performance of neural network potentials for reduction-potential and electron-affinity, suggesting potential advantages over DFT and SQM methods.

Benchmarking OMol25-Trained Models on Experimental Reduction-Potential and Electron-Affinity Data

Discussion

Member takes

Implication

Key Points

Cite This Study

Benchmarking OMol25-Trained Models on Experimental Reduction-Potential and Electron-Affinity Data

Discussion

Member takes

Implication

Key Points

Cite This Study

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