Aug 2026· Journal of Materials Science: Materials Theory· Vol 10· 0 citations· 137 references
Metal-Organic Frameworks: Synthesis and Applications
Abstract
Metal organic frameworks (MOFs) have emerged as promising electrode materials for supercapacitor (SC) due to their high surface areas, tunable porosity, and redox active sites. However, the vast chemical space of MOFs leads to millions of possible structures, makes experimental trial and error discovery inefficient. This review provides a focused perspective on how density functional theory (DFT) and machine learning (ML) are enabling the accelerated discovery and rational design of MOF-based SC electrodes. Key insights from DFT are discussed in relation to three critical performance descriptors as electrical conductivity, electrochemical and structural stability, and redox activity. In parallel, recent advances in ML-driven screening are reviewed, covering the development and use of large-scale MOF databases, descriptor engineering strategies, and predictive model architectures. Case studies demonstrating the successful integration of DFT and ML for identifying high-performance MOFs are highlighted in the review. Finally, the current limitations are analysed, including the discrepancy between idealized computational models and real polycrystalline electrodes, intrinsic trade-offs between conductivity and stability, and the need for interpretable and physics-informed ML models. Overall, this review outlines a computational roadmap for the rapid discovery and optimization of next-generation MOF-based SC electrodes. This is the comprehensive review on ML-driven prediction of electrochemical performance in MOF based electrode for SC applications. It integrates DFT-calculated electronic descriptors with ML models to discover hidden structure-property relationships. It identifies critical data gaps, model transferability issues, lack of dynamic ion-transport modelling in current studies. It proposed a multi-fidelity active learning framework combining DFT, ML and experiments for accelerated MOF discovery. It outlines standardized database protocols and explainable AI strategies to guide future high-performance MOF design.
Emerging opportunities in physics-informed machine learning, graph neural networks, generative artificial intelligence, active learning, and autonomous closed-loop DFT-ML-MKM workflows are discussed as promising directions for accelerating the discovery of next-generation electrocatalysts with enhanced activity, selectivity, and long-term stability.
Swetarekha Ram, Shalini Tomar, S. Bhattacharjee· Chemical Communications· 0 citations
Supercapacitors (SCs) are critical for high-power energy storage, yet their practical deployment is still limited by insufficient energy density. MXenes have emerged as promising electrode materials owing to inherent metallic conductivity, hydrophilic terminations, and intercalation pseudocapacitance, but suffer from layer restacking and oxidative degradation. Hybridizing MXenes with 3D carbon scaffolds offers a synergistic strategy by providing interlayer spacers, hierarchical ion transport pathways, and oxidation barriers. To the best of our knowledge, this review is the first to systematically couple the structural design of MXene/3D-carbon composites with machine learning (ML) guided optimization, summarizing recent advances in MXene/3D-carbon composite electrodes. This review systematically evaluates MXene etching routes and representative composite assembly strategies, and classifies existing systems into three structural categories, critically comparing their performance metrics, strengths, and inherent limitations. It further highlights the frontier applications of ML in performance prediction, compositional optimization, and mechanical design, along with current challenges, including data bias, the black-box nature of models, and the gap between idealized predictions and real synthesis. Overall, this work aims to provide a framework for integrating advanced synthesis strategies, 3D architectures, and data-driven tools toward the rational design of high-performance SCs.
Boyuan Mu, Z. Hou, Shuhan Shi· Smart Materials and Devices· 0 citations
The solid-state electrolytes (SSE) are gaining tremendous attention in designing rechargeable batteries with remarkable energy density and safety features for next-generation energy storage device applications. The rational design of SSE with promising ionic conductivity, higher electrochemical stability windows, and stable electrode-electrolyte interfaces remain a formidable challenge, traditionally hindered by trial-and-error experimentation and computationally expensive theoretical simulations. Here, we systematically review the recent breakthroughs in the artificial intelligence (AI)-driven design of SSE, spanning electrochemical stability and ionic conductivity domains, with a particular focus on how machine learning (ML) and deep learning (DL) are fundamentally transforming the discovery and optimization landscape. We critically discuss the synergy between first-principles density functional theory (DFT), molecular dynamics (MD) simulations, and advanced AI algorithms including supervised and unsupervised learning (SL, UL), graph neural networks (GNNs), and Machine Learning Interatomic Potentials (MLIP) that collectively enable accurate prediction of ionic conductivity, elucidation of ion transport mechanisms, and high-throughput screening (HTS) of vast chemical spaces. Emphasis is placed on descriptor engineering that bridges atomic-level structural features (e.g., lattice parameters, activation energies, defect chemistry) with macroscopic electrochemical performance, as well as the emerging paradigm of closed-loop, self-driving laboratories for autonomous materials discovery. Furthermore, AI-guided strategies have demonstrated remarkable interfacial ionic transport mechanism. Despite these transformative advances, persistent challenges including data scarcity, limited descriptor transferability, discrepancies between theoretical predictions and experimental realization, remain significant challenges. Looking forward, the convergence of AI with high-throughput experimentation and multiscale modeling promises to redefine SSE discovery, accelerating the deployment of high-performance all solid-state batteries (ASSBs) for sustainable energy storage.
Jiaying He, Zama Jan, Heqin Guo et al.· AI for Materials· 0 citations
Volatile organic compounds (VOCs) are major precursors of ozone and fine particulate matter, posing significant risks to both environmental quality and human health. Catalytic oxidation is widely regarded as one of the most effective approaches for VOC abatement; however, the rational design of efficient catalysts remains a central challenge. In this review, we present a perspective on MOF-based catalysts by addressing three fundamental questions in catalyst design: where the active sites are located, how they function, and how their stability can be maintained. We show that metal-organic frameworks (MOFs), owing to their tunable coordination environments and porous architectures, provide unique opportunities to precisely define and regulate active sites. Early studies focus on dispersing active species on MOFs to control their location, while subsequent advances emphasize interfacial and electronic regulation to improve catalytic function. More recently, MOF-derived catalysts and photothermal systems have been developed to enhance structural stability and enable efficient energy utilization. By integrating representative studies from the literature with our own contributions, we review how MOF-based systems evolve from simple active-site carriers to structurally and functionally integrated catalysts. This perspective provides a unified framework for understanding structure-function-stability relationships in VOC oxidation and offers guidance for the design of next-generation catalytic systems.
Shuchen Liu, Qinye Fang, Jin-Ming Luo et al.· Chemical Communications· 0 citations
Cobalt & cobalt based hybrid nanostructured materials have attracted interest of researcher and scientist for supercapacitor applications. They have hierarchical architecture and high pseudocapacitive performance resulted in better store energy than a regular capacitor as well as charge/discharge much faster than a battery. However, the nonlinear correlation between specific capacitance and synthesis conditions, structural morphology and electrochemical parameters relying on conventional experimental approach insufficient for material design. Thereby necessitating data-driven and machine learningassisted strategies are preferred. This review examines the application of machine learning as a systematic framework for characterizing of materials, predicting and optimizing the electrochemical performance of cobalt based supercapacitor system. Herein, ML model selection on charge storage mechanisms, material classifications and relevant synthesis and characterization techniques are discussed.
Arsh Sardana, Chhavi Sharma, Kunal et al.· 2026 11th International Conf...· 0 citations
Two‐Stage Material Screening (TSMS) is developed, an AI‐driven framework that integrates density functional theory (DFT) computations, an active‐learning‐guided experimental feedback loop, and mechanistic interpretation to enable rapid discovery and systematic evaluation of promising electrocatalysts.
Xueyu Hu, Yucun Zhou, Haoyu Li et al.· Advances in Materials· 0 citations
A new method, called CW-Net, translates the reasoning process of an autonomous vehicle’s AI system into understandable concepts that explain its behavior.