Interpretable, Low-Compute Machine Learning Integrating Experimental and Catalytic Descriptors for Sustainable CO2 Electroreduction

Brianna Farris; Joshua J Meckstroth; Kevin C. Leonard

doi:10.1039/d6gc01753c

January 1, 2026Green ChemistryOpen Access

Interpretable, Low-Compute Machine Learning Integrating Experimental and Catalytic Descriptors for Sustainable CO2 Electroreduction

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Key Points

The aim is to develop a machine learning framework that is both interpretable and requires low computational resources for sustainable CO2 electroreduction.
Combined pre-trained models for information extraction with catalytic descriptors
Employed low-compute machine learning techniques to reduce energy demands
Focused on integrating experimental data with computational predictions.
Demonstrated effective reduction of CO2 using low-compute models
Improved interpretability of electroreduction processes
Highlighted pathways for enhancing sustainability in green chemistry.

Implication

Study integrates machine learning with experimental descriptors for CO2 reduction, suggesting innovative sustainability pathways.

Abstract

Applying machine learning (ML) sustainably to green chemistry is challenging because reaction complexity often drives the use of large, energy-intensive models. Here, we combine pre-trained models for information extraction with...

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Cite This Study

Farris et al. (2026) studied this question.

synapsesocial.com/papers/6a2a508980c8f91e7f39d127 https://doi.org/10.1039/d6gc01753c

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Interpretable, Low-Compute Machine Learning Integrating Experimental and Catalytic Descriptors for Sustainable CO2 Electroreduction

Key Points

Implication

Abstract

Discussion

Cite This Study

Interpretable, Low-Compute Machine Learning Integrating Experimental and Catalytic Descriptors for Sustainable CO2 Electroreduction

Key Points

Implication

Abstract

Discussion

Cite This Study

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