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Preparation of SNS CobaltII Pincer Model Complexes of Liver Alcohol Dehydrogenase

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Stability Trends in disubstituted Cobaltocenium Based on the Analysis of the Machine Learning Models.

Shehani T Wetthasinghe¹, Sophya V Garashchuk¹, Vitaly A Rassolov¹

¹Department of Chemistry and Biochemistry, University of South Carolina, Columbia, South Carolina 29208, United States.

The Journal of Physical Chemistry. A

|November 28, 2023

Summary

Machine learning models predict the stability of cobaltocenium derivatives for fuel cells. Key molecular features accurately estimate bond dissociation energies, accelerating materials discovery.

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Published on: May 29, 2018

Area of Science:

Materials Science
Computational Chemistry
Electrochemistry

Background:

Cobaltocenium derivatives show promise for anion exchange membranes in fuel cells due to stability and anion mobility.
Tuning properties via substituents on cobaltocenium cations is complex and time-consuming.
High-level computational screening of electronic structure is computationally intensive.

Purpose of the Study:

To develop machine learning models for predicting the stability of disubstituted cobaltocenium-hydroxide complexes.
To identify key molecular features that govern the bond-dissociation energy (BDE) of these complexes.
To explore a fragment-based approach for efficient BDE modeling.

Main Methods:

Utilized machine learning (ML) to predict the BDE of cobaltocenium-hydroxide complexes.
Analyzed a dataset of all possible disubstituted cobaltoceniums, expanding on previous studies.
Identified key predictive features including molecular orbitals and atomic charges.

Main Results:

Highest occupied and lowest unoccupied molecular orbitals, and Hirshfeld charge on substituted benzene, are key BDE predictors.
Acidity of substituents significantly impacts cobaltocenium stability and model performance.
Refined ML models predict BDE with high accuracy (~1 kcal/mol) using fragment properties.

Conclusions:

ML modeling effectively predicts cobaltocenium stability, accelerating the design of new materials.
A fragment-based approach significantly reduces computational cost and time for BDE calculations.
This strategy is crucial for developing advanced materials for fuel cell applications.