A Systematic Approach for Incorporating Structural Flexibility in High-Throughput Computational Screening of Metal-Organic Frameworks for Xylene Separation.

Separation of xylene isomers poses a significant challenge due to their similar physicochemical properties. Currently, zeolites are utilized as adsorbents for this purpose in the chemical industry despite suboptimal separation performance. With tunable pore size and chemical functionality, metal-organic frameworks (MOFs) are promising adsorbents for separation.

Hydrostable Fluorinated Metal-Organic Frameworks for CO Capture from a Wet Flue Gas: Multiscale Computational Screening.

Metal-organic frameworks (MOFs) are promising adsorbents for CO capture due to readily tunable porosity and diverse functionality; however, their performance is deteriorated by the presence of HO in a flue gas. Fluorinated MOFs (FMOFs) may impede HO interaction with frameworks and enhance CO adsorption under humid conditions. In this study, a multiscale computational screening study is reported to identify the top FMOFs for CO capture from a wet flue gas.

Toward a Generalizable Machine-Learned Potential for Metal-Organic Frameworks.

Machine-learned potentials (MLPs) have transformed the field of molecular simulations by scaling "quantum-accurate" potentials to linear time complexity. While they provide more accurate reproduction of physical properties as compared to empirical force fields, it is still computationally costly to generate their training data sets from ab initio calculations. Despite the emergence of foundational or general MLPs for organic molecules and dense materials, it is unexplored if one general MLP can be effectively developed for a wide variety of nanoporous metal-organic frameworks (MOFs) with different chemical moieties and geometric properties.

Accelerating Discovery of Mechanically Stable Metal-Organic Frameworks for Vinylidene Fluoride Storage by Active Learning.

Metal-organic frameworks (MOFs) are versatile nanoporous materials for a wide variety of important applications. Recently, a handful of MOFs have been explored for the storage of toxic fluorinated gases (Keasler et al. 1455), yet the potential of a great number of MOFs for such an environmentally sustainable application has not been thoroughly investigated.

Nanoscopic Insights into the Collapse of Metal-Organic Frameworks during Solvent Evacuation: Molecular Simulation Investigation.

Many metal-organic frameworks (MOFs) undergo structural collapse upon solvent evacuation during activation, which is attributed to the capillary force generated by the solvent. However, little effort has been devoted to unveiling the nature of such a force. Herein, we employ molecular dynamics (MD) simulations to investigate the evacuation of different solvents in two MOFs (MOF-5 and UMCM-9).

Exploiting Metal-Organic Frameworks for Vinylidene Fluoride Adsorption: From Force Field Development, Computational Screening to Machine Learning.

Metal-organic frameworks (MOFs) represent a distinctive class of nanoporous materials with considerable potential across a wide range of applications. Recently, a handful of MOFs has been explored for the storage of environmentally hazardous fluorinated gases (Keasler et al. 2023, 381, 1455), yet the potential of over 100,000 MOFs for this specific application has not been thoroughly investigated, particularly due to the absence of an established force field.

Accelerating Discovery of Water Stable Metal-Organic Frameworks by Machine Learning.

Metal-organic frameworks (MOFs) provide an extensive design landscape for nanoporous materials that drive innovation across energy and environmental fields. However, their practical applications are often hindered by water stability challenges. In this study, a machine learning (ML) approach is proposed to accelerate the discovery of water stable MOFs and validated through experimental test.

Classifying and Predicting the Thermal Expansion Properties of Metal-Organic Frameworks: A Data-Driven Approach.

Metal-organic frameworks (MOFs) are versatile materials for a wide variety of potential applications. Tunable thermal expansion properties promote the application of MOFs in thermally sensitive composite materials; however, they are currently available only in a handful of structures. Herein, we report the first data set for thermal expansion properties of 33,131 diverse MOFs generated from molecular simulations and subsequently develop machine learning (ML) models to (1) classify different thermal expansion behaviors and (2) predict volumetric thermal expansion coefficients (α).

Understanding the Structural Collapse during Activation of Metal-Organic Frameworks with Copper Paddlewheels.

Many metal-organic frameworks (MOFs) suffer from stability issues as they can be easily amorphized from various external stimuli. In particular, it is common to observe structural collapse during the activation process of removing the synthesis solvent. In this study, we conduct high-throughput computational analysis that focuses on the activation status of MOFs that possess copper paddlewheel metal nodes.