Computational design of Metal-Organic Frameworks for sustainable energy and environmental applications: Bridging theory and experiment

Abstract

This review explores the pivotal role of computational approaches in designing and developing Metal-Organic Frameworks (MOFs) for sustainable energy and environmental applications. As demand for advanced materials in energy conversion, storage, and environmental remediation intensifies, the synergy between theoretical simulations and experimental research has become critical. We provide a systematic overview of recent advancements in computational strategies guiding MOF synthesis and optimization, focusing on how these approaches offer insights into MOF mechanisms and working principles. The review examines fundamental computational techniques, including density functional theory, molecular dynamics, and machine learning, exploring their application in predicting and enhancing MOF performance for gas storage, catalysis, and pollutant capture. Through analysis of case studies, we demonstrate how computational modeling has successfully improved MOF performance in real-world scenarios. We also address challenges in bridging theory and experiment, discussing strategies for enhancing model accuracy and applicability.