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Metal-organic frameworks (MOFs) are a specific class of hybrid, crystalline, nano-porous materials made of metal-ion-based nodes and organic linkers. Most of the studies on MOFs largely focused on porosity, chemical and structural…
Metal-organic frameworks (MOFs) are promising materials for methane capture due to their high surface area and tunable properties. Metal substitution represents a powerful strategy to enhance MOF performance, yet systematic exploration of…
Covalent organic frameworks (COFs) are promising adsorbents for gas adsorption and separation, while identifying the optimal structures among their vast design space requires efficient high-throughput screening. Conventional…
Carbon mitigation is one challenging issue that the world is facing. To tackle deleterious impacts of CO2, processes emerged, including chemisorption from amine based solvents, and more recently physisorption in porous solids. While CO2…
Metal-organic frameworks (MOFs) have emerged as promising materials for carbon capture applications due to their high CO2 capacities and tunable properties. Amongst the many possible MOFs, metal-substituted compounds based on M-DOBDC and…
The enormous structural and chemical diversity of metal-organic frameworks (MOFs) forces researchers to actively use simulation techniques on an equal footing with experiments. MOFs are widely known for outstanding adsorption properties, so…
We combine infrared spectroscopy, nano-indentation measurements, and \emph{ab initio} simulations to study the evolution of structural, elastic, thermal, and electronic responses of the metal organic framework MOF-74-Zn when loaded with…
In this introductory review, we give an overview of the computational chemistry methods commonly used in the field of metal-organic frameworks (MOFs), to describe or predict the structures themselves and characterize their various…
Addressing climate change calls for action to control CO$_2$ pollution. Direct air and ocean capture offer a solution to this challenge. Making carbon capture competitive with alternatives, such as forestation and mineralisation, requires…
Recently, metal-organic frameworks (MOFs) have demonstrated their potential as solid-state electrolytes in proton exchange membrane fuel cells. However, the number of MOFs reported to exhibit proton conductivity remains limited, and the…
Coordination polymerization leads to various metal-organic frameworks (MOFs) in which symmetrical metal nodes exposed to nano voids lead to unique physical properties and chemical functionalities. One of the challenges towards their…
In a world where capture and separation processes represent above 10% of global energy consumption, novel porous materials, such as Metal-Organic Frameworks (MOFs) used in adsorption-based processes are a promising alternative to dethrone…
Machine learning has emerged as a powerful approach in materials discovery. Its major challenge is selecting features that create interpretable representations of materials, useful across multiple prediction tasks. We introduce an…
Metal-organic frameworks (MOFs) are highly porous and versatile materials studied extensively for applications such as carbon capture and water harvesting. However, computing phonon-mediated properties in MOFs, like thermal expansion and…
Metal-organic frameworks (MOFs) are a family of materials that have high porosity and structural tunability and hold great potential in various applications, many of which requiring a proper understanding of the thermal transport…
Outstanding functional tunability underpinning metal-organic framework (MOF) confers a versatile platform to contrive next-generation chemical sensors, optoelectronics, energy harvesters and converters. We report a rare exemplar of a porous…
The removal of leaked radioactive iodine isotopes in humid environments holds significant importance in nuclear waste management and nuclear accident mitigation. In this study, high-throughput computational screening and machine learning…
We present a computational microscopy analysis (targeted molecular dynamics simulations) of the structure and performance of conductive metal organic framework (MOF) electrodes in supercapacitors with room temperature ionic liquids. The…
Metal-organic frameworks (MOFs) are porous crystalline materials with broad applications such as carbon capture and drug delivery, yet accurately predicting their 3D structures remains a significant challenge. While Large Language Models…
Graph neural networks (GNNs) have drawn more and more attention from material scientists and demonstrated a high capacity to establish connections between the structure and properties. However, with only unrelaxed structures provided as…