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Direct air capture of Carbon Dioxide is a technical solution that does not rely on natural processes to capture CO2 from the atmosphere. In DAC, the filter material is designed to specifically bind CO2 molecules. Hence a high-capacity…

Quantum Physics · Physics 2023-11-22 Gopal Ramesh Dahale

Carbon capture is vital for decarbonizing heavy industries such as steel and chemicals. Metal-organic frameworks (MOFs), with their high surface area and structural tunability, are promising materials for CO2 capture. This study focuses on…

Due the alarming rate of climate change, the implementation of efficient CO$_2$ capture has become crucial. This project aims to create an algorithm that predicts the uptake of CO$_2$ adsorbing Metal-Organic Frameworks (MOFs) by using…

Machine Learning · Computer Science 2021-10-13 Mahati Manda

Despite the recent progress in quantum computational algorithms for chemistry, there is a dearth of quantum computational simulations focused on material science applications, especially for the energy sector, where next generation sorbing…

The large number of possible structures of metal-organic frameworks (MOFs) and their limitless potential applications has motivated molecular modelers and researchers to develop methods and models to efficiently assess MOF performance. Some…

Materials Science · Physics 2021-10-04 Krishnendu Mukherjee , Alexander W. Dowling , Yamil Colón

Quantum-mechanical simulations can offer atomic-level insights into chemical processes on surfaces. This understanding is crucial for the rational design of new solid catalysts as well as materials to store energy and mitigate greenhouse…

Understanding how structural flexibility affects the properties of metal-organic frameworks (MOFs) is crucial for the design of better MOFs for targeted applications. Flexible MOFs can be studied with molecular dynamics simulations, whose…

Materials Science · Physics 2024-05-13 Abhishek Sharma , Stefano Sanvito

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…

Materials Science · Physics 2021-11-22 Vadim V. Korolev , Yurii M. Nevolin , Thomas A. Manz , Pavel V. Protsenko

Metal-organic frameworks (MOFs) are of immense interest in applications such as gas storage and carbon capture due to their exceptional porosity and tunable chemistry. Their modular nature has enabled the use of template-based methods to…

Chemical Physics · Physics 2023-10-19 Xiang Fu , Tian Xie , Andrew S. Rosen , Tommi Jaakkola , Jake Smith

Direct air capture (DAC) of carbon dioxide is a promising method for mitigating climate change. Solid sorbents, such as metal-organic frameworks, are currently being tested for DAC application. However, their potential for deployment at…

Quantum Physics · Physics 2024-12-05 Marco Antonio Barroca , Rodrigo Neumann Barros Ferreira , Mathias Steiner

Metal-organic frameworks (MOFs) are a class of crystalline materials with promising applications in many areas such as carbon capture and drug delivery. In this work, we introduce MOFFlow, the first deep generative model tailored for MOF…

Biomolecules · Quantitative Biology 2025-03-20 Nayoung Kim , Seongsu Kim , Minsu Kim , Jinkyoo Park , Sungsoo Ahn

Metal-organic frameworks (MOFs) are porous materials composed of metal ions and organic linkers. Due to their chemical diversity, MOFs can support a broad range of applications in chemical separations. However, the vast amount of structural…

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…

Materials Science · Physics 2016-02-02 François-Xavier Coudert , Alain H. Fuchs

The article summarizes the study performed in the context of the Deloitte Quantum Climate Challenge in 2023. We present a hybrid quantum-classical method for calculating Potential Energy Surface scans, which are essential for designing…

Quantum Physics · Physics 2023-09-12 Kourosh Sayar Dogahe , Tamara Sarac , Delphine De Smedt , Koen Bertels

Metal-organic frameworks (MOFs) are a highly tunable class of crystalline materials where metal atoms or clusters are connected by organic linkers. They offer a versatile platform for exploring quantum phenomena such as entangled magnetism,…

Strongly Correlated Electrons · Physics 2025-12-25 Natalia Drichko , V. Sara Thoi , N. Peter Armitage

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…

Materials Science · Physics 2016-03-11 Hyun Seung Koh , Malay Kumar Rana , Jinhyung Hwang , Donald J. Siegel

The discovery of Metal-Organic Frameworks (MOFs) with application-specific properties remains a central challenge in materials chemistry, owing to the immense size and complexity of their structural design space. Conventional computational…

Machine Learning · Computer Science 2025-06-03 Srivathsan Badrinarayanan , Rishikesh Magar , Akshay Antony , Radheesh Sharma Meda , Amir Barati Farimani

Modeling electronic systems is an important application for quantum computers. In the context of materials science, an important open problem is the computational description of chemical reactions on surfaces. In this work, we outline a…

Covalent Organic Frameworks (COFs) are versatile two-dimensional (2D) materials for flexible electronics, catalysis, and sensing, owing to their tunable architectures and large surface areas. However, like most materials, COFs inevitably…

Quantum simulations of metal surfaces are critical for catalytic innovation. Yet existing methods face a cost-accuracy dilemma: density functional theory is efficient but system-dependent in accuracy, while wavefunction-based theories are…

Chemical Physics · Physics 2026-04-10 Changsu Cao , Hung Q. Pham , Zhen Guo , Yutan Zhang , Zigeng Huang , Xuelan Wen , Ji Chen , Dingshun Lv
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