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 CO2 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, CO2 reduction, and ammonia synthesis. Simultaneously, ML applications in CO2 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 CO2 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.
The accelerating global demand for high-performance energy storage systems has stimulated significant research into advanced polymer composites as next-generation electrolytes, electrode binders, and functional membranes for batteries, supercapacitors, and photovoltaic devices. However, the vast compositional and structural design space of polymer materials presents formidable challenges for conventional trial-and-error discovery strategies, which remain slow, costly, and biased by prior expert knowledge. Machine learning (ML) and artificial intelligence (AI) have emerged as transformative tools for navigating this complexity, enabling rapid prediction of electrochemical properties, de novo design of polymer electrolytes, and precise optimization of nanostructures for supercapacitors and batteries. This review systematically examines the application of ML techniques, including graph neural networks, Bayesian optimization, variational autoencoders, and transformer-based language models, for the discovery of energy storage polymer composites. The discussion critically evaluates ML-driven advancements across lithium-ion batteries, flexible energy storage devices, and solar energy materials, drawing on quantitative performance benchmarks reported in the primary literature. Emerging strategies such as active learning, multi-fidelity data fusion, physics-informed neural networks, and polymer-specific foundation models are discussed alongside persistent challenges related to data scarcity, model interpretability, and the translation gap between computational prediction and experimental synthesis. The review further addresses the landscape of open polymer property databases, the role of autonomous closed-loop experimentation in accelerating materials discovery, and the importance of reproducible, well-documented machine learning pipelines for the field to mature beyond proof-of-concept demonstrations. By consolidating evidence from verified primary sources and presenting original comparative analyses across methods and application domains, this review provides researchers, materials scientists, and computational chemists with an actionable, evidence-based perspective on the current state and future trajectory of AI-accelerated, sustainable energy storage polymer composite discovery.
Manas Kumar Yogi, D. Uma, Yamuna Mundru et al.· Journal of Polymer & Composi...· 0 citations
The intersection of machine learning (ML) and electrochemical research is catalyzing transformative advancements in energy storage and conversion technologies. This review critically examines the role of ML and deep learning (DL) in optimizing electrochemical systems, focusing on batteries, supercapacitors, fuel cells, and sensors. ML-driven approaches facilitate accelerated material discovery, precise property predictions, and enhanced device performance monitoring, surpassing conventional trial-and-error methodologies. Integrating computational materials science, including density functional theory (DFT) and molecular dynamics (MD), with ML enables predictive modelling of electrochemical processes at an unprecedented scale. However, challenges such as data heterogeneity, model interpretability, and computational cost continue to limit widespread adoption. This review identifies key strategies to overcome these barriers, including establishing standardized data repositories, developing hybrid physics-informed ML models, and implementing explainable AI (XAI) for enhanced model transparency. By addressing these challenges, ML has the potential to drive the next wave of breakthroughs in electrochemical energy storage and conversion, accelerating the transition toward a sustainable and energy-secure future.
Unknown authors· Journal of the Electrochemic...· 0 citations
Artificial intelligence (AI) has emerged as a transformative technology capable of addressing complex engineering challenges through advanced data-driven modeling, prediction, and optimization techniques. In the context of sustainable energy systems, hydrogen production via water electrolysis has attracted considerable attention as a promising pathway toward carbon-neutral energy generation. However, the widespread deployment of electrolyzer technologies remains constrained by challenges related to energy efficiency, operational stability, system degradation, and dynamic process control. In response, machine learning (ML) techniques have increasingly been integrated into electrolyzer systems to enhance performance prediction, adaptive control, predictive maintenance, and operational optimization. This review presents a comprehensive and engineering-oriented analysis of recent advancements in ML applications for hydrogen production via electrolysis. The study first outlines the fundamental principles of ML, including major learning paradigms, data processing approaches, and commonly adopted algorithms for electrochemical systems. Subsequently, the review critically examines ML-driven strategies for hydrogen production optimization, hyperparameter tuning, intelligent process control, system design enhancement, and degradation monitoring in electrolyzer technologies such as Proton Exchange Membrane Water Electrolyzers (PEMWE) and Anion Exchange Membrane Water Electrolyzers (AEMWE). Comparative analysis of algorithms including Artificial Neural Networks (ANN), Support Vector Machines (SVM), Reinforcement Learning (RL), and Physics-Informed Neural Networks (PINN) is also discussed from both electrochemical and engineering perspectives. Furthermore, current challenges involving data quality, model interpretability, computational complexity, and industrial deployment are critically evaluated. Overall, this review provides strategic insights into future intelligent electrolyzer systems and highlights critical research directions for scalable and sustainable green hydrogen production.
Muhammad Asyraf Abdullah, A. Sulong, E. Majlan et al.· Jurnal Kejuruteraan· 0 citations
The proposed framework combines data preprocessing, feature engineering, predictive modeling, optimization, and sustainability assessment to identify materials that satisfy both engineering and environmental requirements.
Iyengar P.K· International Journal of Mod...· 0 citations
The discovery of advanced alloys capable of withstanding extreme environmental conditions such as high temperatures, intense radiation, corrosive atmospheres, and mechanical stress is critical for applications in aerospace, nuclear energy, deep-sea exploration, and space missions. Traditional experimental and computational approaches to alloy design are often time-consuming and resource-intensive. Recent advances in artificial intelligence (AI) and machine learning (ML) offer powerful tools to accelerate the discovery process by enabling high-throughput screening, property prediction, and design optimization. This paper presents a comprehensive review and methodology for AI-assisted alloy discovery, focusing on the integration of data-driven models with physical principles, high-fidelity simulations, and experimental validation. We highlight successful case studies, discuss the challenges of data scarcity and model interpretability, and propose a framework for closed-loop design that incorporates generative models and active learning. This AI-driven approach represents a paradigm shift toward faster, more cost-effective discovery of next-generation materials for extreme environments.
Venkatesh Iyer, Nandhini Ravi· International Journal of Mod...· 0 citations
Abstract - Heat exchangers are critical components in industries such as power generation, chemical processing, refrigeration, automotive engineering, and renewable energy systems. Their performance directly influences energy efficiency, operational costs, and environmental sustainability. Conventional design and optimization techniques often rely on empirical correlations, trial-and-error methods, or computationally intensive simulations, which may be time-consuming and unable to efficiently explore complex design spaces. Recent advancements in Artificial Intelligence (AI) have introduced innovative approaches for optimizing heat exchanger design, operation, and maintenance. AI techniques, including Artificial Neural Networks (ANNs), Genetic Algorithms (GAs), Particle Swarm Optimization (PSO), Support Vector Machines (SVMs), and Deep Learning (DL), enable rapid prediction of thermal performance, optimization of geometric parameters, and intelligent fault detection while reducing computational effort. This paper presents a comprehensive review of AI-based optimization methods for heat exchangers, highlighting their applications in thermal performance enhancement, energy efficiency improvement, pressure drop minimization, and predictive maintenance. The study also discusses the advantages, limitations, and future prospects of integrating AI with Computational Fluid Dynamics (CFD), Internet of Things (IoT), and digital twin technologies for real-time monitoring and adaptive optimization. The findings indicate that AI-driven optimization can significantly improve heat transfer efficiency, reduce energy consumption, lower operational costs, and support sustainable industrial processes. The paper concludes that AI has emerged as a transformative technology for next-generation intelligent heat exchanger systems, offering substantial potential for research and industrial implementation.
Key Words: AI, GA, ANN,DL, SVM.
H. Hedau, Omkareshwar Mahate, Omkar Kokate et al.· International Scientific Jou...· 0 citations
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