Jul 2026· Journal of the American Chemical Society· Vol 148 31, pp.
33284-33294
· 1 citation· 78 references
Medicine
Abstract
We report an end-to-end computational-experimental workflow for the discovery of metal-organic frameworks (MOFs), demonstrated by the computational design and synthesis of two novel Zn-based frameworks, UCHI-1 and UCHI-2, exhibiting enhanced methane uptake and selectivity at low pressure under ambient conditions (298 K, 1 bar). The workflow enables the rational selection and experimental realization of metal-organic frameworks combining data mining, machine-learning driven adsorption prediction, and structure generation, with experimental synthesis and validation within a closed-loop discovery pipeline. Analysis of existing and newly generated MOFs reveals the structure-property relationships governing low-pressure methane adsorption, identifying an optimal pore size and shape, framework densities, linker functionalities, and framework topologies that maximize dispersive C-H/π and van der Waals interactions. Beyond the specific materials identified herein, the results establish this workflow as a scalable and extensible platform for accelerated MOF discovery, with clear routes toward further optimization and automation while demonstrating practical applicability beyond purely theoretical exploration of hypothetical materials.
Increasing global CO2 emissions are driving efforts to develop advanced carbon capture materials. Metal-organic frameworks (MOFs) show great promise as CO2 adsorbents, yet maintaining performance under humid flue gas conditions remains a major challenge. Herein, we present an integrated computational high-throughput screening workflow that begins with a library of over 110,000 experimental MOFs or MOF-like structures, explicitly includes the effects of water in adsorption simulations, and incorporates machine learning-based stability analysis to identify promising candidates from among existing MOFs. Guided by this workflow, we synthesized a top-performing MOF that demonstrates exceptional tolerance to humid conditions, maintaining high CO2 uptake at elevated relative humidity. We further revealed key structure-property relationships and structural motifs that offer valuable design principles for next-generation MOFs for CO2 capture in humid conditions.
Jiayang Liu, Xiaoliang Wang, Xiyang Liu et al.· Journal of the American Chem...· 0 citations
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.
Achal Siddharth Fulmali, H. Panda· Journal of Materials Science...· 0 citations
A mechanism-driven approach to alleviate data dependence and develop a multisource transfer learning (MS-TL) framework that leverages the knowledge embedded in abundant adsorption data sets while accurately capturing local structural dependence, enabling a deep fusion of multidimensional thermodynamic knowledge while preserving local structural information.
Wangqiang Lin, Huiyan Zhang, Jinxin Sun et al.· Journal of the American Chem...· 0 citations
Hydrogen (H2) is expected to play a critical role in modern industry, particularly in ammonia synthesis, petroleum refining, and low-carbon transportation. The safe storage of H2 remains a major challenge due to its low volumetric density under ambient conditions. Metal–Organic Frameworks (MOFs), highly porous crystalline materials, have emerged as promising H2 storage candidates owing to their high surface areas and tuneable pore structures. Molecular simulations such as grand canonical Monte Carlo or density functional theory are costly and limited in exploring large material spaces, motivating efficient predictive tools to accelerate discovery. Here, Machine Learning (ML) techniques are compared to an explainable artificial intelligence (XAI) approach using symbolic regression (SR), trained on 10,123 experimentally measured H2 adsorption datapoints from real-world MOFs. The best performing model achieved a goodness of fit of 0.9986 with lower computational demand, but reduced interpretability, addressed using XAI analysis and clustering. SR achieves a lower goodness of fit of 0.914 but produces a physically meaningful equation highlighting structural features driving high gravimetric efficiencies. These results demonstrate strong ML capability for predicting how MOF properties and environmental conditions affect H2 uptake. This offers engineers and researchers a practical means of screening potential MOFs for H2 storage applications, with the XAI analyses providing additional confidence in the predictions. They allow researchers to understand the physical reasoning behind each output, assess the reliability of individual predictions, and make fully informed decisions, enabling predictive models to be acted upon with confidence in real-world contexts.
R. Taylor, Shahin Alipour Bonab, M. Yazdani-Asrami· Algorithms· 0 citations
Metal-organic frameworks (MOFs) and covalent organic frameworks (COFs) are highly tunable in pore structure and chemical environment, yet their discovery remains slow and fragmented. Synthesis reports are often difficult to compare, characterization data are laborious to interpret, and computational predictions rarely guide experiments directly. Recent advances in large language models (LLM) have enabled the development of artificial intelligence (AI) agents that can interpret research goals, search the literature and databases, call external tools, and adapt workflows based on intermediate results. In this review, we distinguish three stages of AI-agent development in MOFs and COFs research: LLM-native, human-mediated systems; database-grounded, tool-using agents; and experiment-integrated, feedback-driven platforms. This progression reflects increasing scientific grounding and experimental agency. In our view, further progress will depend less on scaling language models alone than on developing traceable machine-actionable data, chemistry-aware validation, persistent experimental memory, and robust interfaces between AI agents and laboratory automation.
Jia-Yu Yu, Zihao Jiang, Donglin He· AI Agent· 0 citations
Ionic liquids (ILs) represent one of the most promising and versatile solvent families for CO2 capture due to their structural tunability and low volatility. However, the sheer scale and complexity of the IL chemical design space make the discovery of high-performance candidates extraordinarily challenging. In this work, we introduce a SHAP-guided Domain Filter (SGDF) framework that leverages machine learning (ML) to address this challenge, enabling reliable large-scale screening across millions of potential IL structures. Starting from over 32 million cation–anion combinations, sequential SGDF gates based on melting point (T m), toxicity, viscosity, and CO2 solubility reduce the search space by more than 2 orders of magnitude, ultimately yielding 69,009 promising candidates. Experimental validation is performed on 12 ILs, including two model-selected candidates that exhibit the desired properties. Notably, the two model-selected ILs exhibit the highest CO2 absorption capacities, surpassing all other samples. Across the 12 ILs evaluated, the framework achieves high predictive accuracy (RMSE = 0.046, MAE = 0.030, R 2 = 0.781), showing excellent agreement between predicted and experimental results. These findings demonstrate that combining predictive modeling with experimental validation offers both mechanistic insight and a scalable, data-driven pathway for designing next-generation sustainable ILs for CO2 capture.
Yushan Chen, Yongsheng Chen· Journal of Physical Chemistr...· 0 citations
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