AI-integrated manufacturing of advanced materials for energy storage, catalysis, and environmental applications
5 affiliations
- 1Department of Materials Science and Engineering, University of Houston
- 2Department of Engineering Data Science, University of Houston
- 3Department of Bioengineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical
- 4Department of Medicine, Medical College of Wisconsin
- 5Department of Biomedical and Environmental Engineering (BEE) California Innovations Corporation (CIC)
Abstract
The incorporation of artificial intelligence (AI) into energy systems has become a transformative strategy for tackling global energy related challenges, particularly energy vulnerability (EVI). This work examines how AI contributes to mitigating EVI by evaluating its influence across several dimensions, including energy availability, operational efficiency, consumption patterns, renewable energy integration, and overall energy security. Based on insights derived from machine learning (ML) enabled developments in catalytic materials and CO 2 capture technologies, this study demonstrates how data-centric approaches expedite material discovery, refine energy processes, and strengthen system resilience. ML methodologies, including artificial neural networks (ANN), support vector regression (SVR), and ensemble learning techniques, exhibit strong predictive performance in estimating activation energies, adsorption properties, and catalytic efficiencies. These methods substantially decrease reliance on computationally intensive density functional theory (DFT) simulations, thereby enabling rapid identification of high-performance catalyst. Moreover, ML-assisted framework supports the detection of active catalytic sites, these optimization of electrocatalytic processes, and the design of materials for hydrogen evolution, CO 2 reduction, and ammonia synthesis. Simultaneously, ML applications in CO 2 capture systems particularly in metal-organic frameworks (MOFs) facilitate high throughput screening and predictive evaluation of adsorption capacity and structural behaviour. Through the application of quantitative structure-property relationships and feature importance analyses, ML models identify key variables governing CO 2 capture performance, thus lowering computational demands and accelerating material development. The study highlights the rise of integrated, closed-loop systems that combine ML, theoretically modelling, and automated experimentation to streamline catalyst development and carbon capture process. Collectively, the results indicate that AI-driven methodologies substantially improve the efficiency, sustainability, and scalability of advanced energy technologies. These developments not only help mitigate energy vulnerability but also promote the global shift toward low-carbon, resilient energy systems.
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BibTeX
@article{punna2026aiintegrated,
title = {{AI-integrated manufacturing of advanced materials for energy storage, catalysis, and environmental applications}},
author = {Sreenivas Punna and Suvarshitha Pusuluru and Madhumita Ravikumar and Farid Menaa},
journal = {Frontiers in Chemistry},
year = {2026},
month = jul,
doi = {10.3389/fchem.2026.1864044},
url = {https://doi.org/10.3389/fchem.2026.1864044},
note = {CC BY},
}
RIS
TY - JOUR
TI - AI-integrated manufacturing of advanced materials for energy storage, catalysis, and environmental applications
AU - Sreenivas Punna
AU - Suvarshitha Pusuluru
AU - Madhumita Ravikumar
AU - Farid Menaa
PY - 2026
DA - 2026/07/03
JO - Frontiers in Chemistry
DO - 10.3389/fchem.2026.1864044
UR - https://doi.org/10.3389/fchem.2026.1864044
AB - The incorporation of artificial intelligence (AI) into energy systems has become a transformative strategy for tackling global energy related challenges, particularly energy vulnerability (EVI). This work examines how AI contributes to mitigating EVI by evaluating its influence across several dimensions, including energy availability, operational efficiency, consumption patterns, renewable energy integration, and overall energy security. Based on insights derived from machine learning (ML) enabled developments in catalytic materials and CO 2 capture technologies, this study demonstrates how data-centric approaches expedite material discovery, refine energy processes, and strengthen system resilience. ML methodologies, including artificial neural networks (ANN), support vector regression (SVR), and ensemble learning techniques, exhibit strong predictive performance in estimating activation energies, adsorption properties, and catalytic efficiencies. These methods substantially decrease reliance on computationally intensive density functional theory (DFT) simulations, thereby enabling rapid identification of high-performance catalyst. Moreover, ML-assisted framework supports the detection of active catalytic sites, these optimization of electrocatalytic processes, and the design of materials for hydrogen evolution, CO 2 reduction, and ammonia synthesis. Simultaneously, ML applications in CO 2 capture systems particularly in metal-organic frameworks (MOFs) facilitate high throughput screening and predictive evaluation of adsorption capacity and structural behaviour. Through the application of quantitative structure-property relationships and feature importance analyses, ML models identify key variables governing CO 2 capture performance, thus lowering computational demands and accelerating material development. The study highlights the rise of integrated, closed-loop systems that combine ML, theoretically modelling, and automated experimentation to streamline catalyst development and carbon capture process. Collectively, the results indicate that AI-driven methodologies substantially improve the efficiency, sustainability, and scalability of advanced energy technologies. These developments not only help mitigate energy vulnerability but also promote the global shift toward low-carbon, resilient energy systems.
SN - 2296-2646
ER -