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Molecular conformer generation is a fundamental task in computational chemistry. Several machine learning approaches have been developed, but none have outperformed state-of-the-art cheminformatics methods. We propose torsional diffusion, a…

Chemical Physics · Physics 2023-03-02 Bowen Jing , Gabriele Corso , Jeffrey Chang , Regina Barzilay , Tommi Jaakkola

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…

We investigate the graph-based convolutional neural network approach for predicting and ranking gas adsorption properties of crystalline Metal-Organic Framework (MOF) adsorbents for application in post-combustion capture of $\textrm{CO}_2$.…

This paper represents a model for microstructure formation in metallic foams based on the multi-phase-field (MPF) approach. By the use of a no-coalescence boundary condition within this MPF-framework, it is possible to completely prevent…

Computational Physics · Physics 2020-07-13 Samad Vakili , Ingo Steinbach , Fathollah Varnik

Infrared-triggered photon upconversion in porous materials presents intriguing prospects for combined functionalities such as molecular sponge, energy harvesting and conversion functionalities. Metal-organic frameworks (MOFs) are one of the…

Cooperative adsorption of gases by porous frameworks permits more efficient uptake and removal than does the more usual non-cooperative (Langmuir-type) adsorption. Cooperativity, signaled by a step-like isotherm, is usually attributed to a…

This technical note describes the FluidFlower concept, a new laboratory infrastructure for geological carbon storage research. The highly controlled and adjustable system produces a strikingly visual physical ground truth of studied…

Metal-organic frameworks (MOFs) are an important class of materials that present intriguing opportunities in the fields of sensing, gas storage, catalysis, and optoelectronics. Very recently, two-dimensional (2D) MOFs have been proposed as…

Materials Science · Physics 2018-10-15 Avijit Kumar , Kaustuv Banerjee , Adam S. Foster , Peter Liljeroth

Metal-organic frameworks (MOFs) are highly promising for carbon capture, yet navigating their vast design space remains challenging. Recent deep generative models enable de novo MOF design but primarily act as feed-forward structure…

Machine Learning · Computer Science 2026-04-16 Chaoran Zhang , Guangyao Li , Dongxu Ji

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…

Accurate prediction of metal-oxide adhesion in high-entropy alloys (HEAs) is challenging because interfacial segregation, atomic environments, and macroscopic thermodynamic quantities are strongly correlated. Relying solely on…

Materials Science · Physics 2026-03-06 Dennis Boakye , Chuang Deng

Pore-scale simulations accurately describe transport properties of fluids in the subsurface. These simulations enhance our understanding of applications such as assessing hydrogen storage efficiency and forecasting CO$_2$ sequestration…

Long-lived emissive materials based on room temperature phosphorescence (RTP) and thermally activated delayed fluorescence (TADF) are considered as the cornerstone of the development of optical sensors, security systems and solid-state…

Materials Science · Physics 2021-10-26 Mario Gutiérrez , Cristina Martín , Johan Hofkens , Jin-Chong Tan

This study delves into the transformation journey of Zr-based Metal-Organic Frameworks (MOFs), focusing on enhancing their mechanical properties and hydrogen storage capacities through doping regulation. MOFs, a versatile class of…

Materials Science · Physics 2026-01-07 Yanhuai Ding , Dan Qian , Zhipeng Liu

Predicting molecular conformations from molecular graphs is a fundamental problem in cheminformatics and drug discovery. Recently, significant progress has been achieved with machine learning approaches, especially with deep generative…

Machine Learning · Computer Science 2022-03-16 Minkai Xu , Lantao Yu , Yang Song , Chence Shi , Stefano Ermon , Jian Tang

Zn(imidazolate)$_2$ metal-organic frameworks (MOFs) exhibit a remarkable degree of polymorphism. Because of their promising industrial applications, many research groups have investigated phase transitions, phase diagram and relative…

Materials Science · Physics 2026-05-01 Emilio Méndez , Rocio Semino

The discovery of inorganic crystal structures with targeted properties is a significant challenge in materials science. Generative models, especially state-of-the-art diffusion models, offer the promise of modeling complex data…

Metal organic frameworks (MOFs) are promising photocatalytic materials due to their high surface area and tuneability of their electronic structure. We discuss here how to engineer the band structures and optical properties of a family of…

Ab initio simulations are combined with in situ infrared spectroscopy to unveil the molecular transport of H$_2$, CO$_2$, and H$_2$O in the metal organic framework MOF-74-Mg. Our study uncovers---at the atomistic level---the major factors…

Materials Science · Physics 2013-01-15 Pieremanuele Canepa , Nour Nijem , Yves J. Chabal , T. Thonhauser

Metal-organic frameworks (MOFs) incorporating open metal sites (OMS) have been identified as promising sorbents for many societally relevant-adsorption applications including CO$_2$ capture, natural gas purification and H$_2$ storage. It is…

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