High-throughput computational screening of metal–organic frameworks

YJ Colón, RQ Snurr - Chemical Society Reviews, 2014 - pubs.rsc.org
There is an almost unlimited number of metal–organic frameworks (MOFs). This creates
exciting opportunities but also poses a problem: how do we quickly find the best MOFs for a …

Large language models for reticular chemistry

Z Zheng, N Rampal, TJ Inizan, C Borgs… - Nature Reviews …, 2025 - nature.com
Reticular chemistry is the science of connecting molecular building units into crystalline
extended structures such as metal–organic frameworks and covalent organic frameworks …

Materials informatics with PoreBlazer v4. 0 and the CSD MOF database

L Sarkisov, R Bueno-Perez, M Sutharson… - Chemistry of …, 2020 - ACS Publications
The development of computational methods to explore crystalline materials has received
significant attention in the last decades. Different codes have been reported to help …

Polymer nanofilms with enhanced microporosity by interfacial polymerization

MF Jimenez-Solomon, Q Song, KE Jelfs… - Nature materials, 2016 - nature.com
Highly permeable and selective membranes are desirable for energy-efficient gas and liquid
separations. Microporous organic polymers have attracted significant attention in this …

N-Aryl–linked spirocyclic polymers for membrane separations of complex hydrocarbon mixtures

KA Thompson, R Mathias, D Kim, J Kim, N Rangnekar… - Science, 2020 - science.org
The fractionation of crude-oil mixtures through distillation is a large-scale, energy-intensive
process. Membrane materials can avoid phase changes in such mixtures and thereby …

Computation-ready, experimental metal–organic frameworks: A tool to enable high-throughput screening of nanoporous crystals

YG Chung, J Camp, M Haranczyk, BJ Sikora… - Chemistry of …, 2014 - ACS Publications
Experimentally refined crystal structures for metal–organic frameworks (MOFs) often include
solvent molecules and partially occupied or disordered atoms. This creates a major …

Metal–organic framework with optimally selective xenon adsorption and separation

D Banerjee, CM Simon, AM Plonka, RK Motkuri… - Nature …, 2016 - nature.com
Nuclear energy is among the most viable alternatives to our current fossil fuel-based energy
economy. The mass deployment of nuclear energy as a low-emissions source requires the …

Smart touchless human–machine interaction based on crystalline porous cages

J Wang, W Lin, Z Chen, VO Nikolaeva, LO Alimi… - Nature …, 2024 - nature.com
The rise of touchless technology, driven by the recent pandemic, has transformed human-
machine interaction (HMI). Projections indicate a substantial growth in the touchless …

Predicting hydrogen storage in MOFs via machine learning

A Ahmed, DJ Siegel - Patterns, 2021 - cell.com
The H 2 capacities of a diverse set of 918,734 metal-organic frameworks (MOFs) sourced
from 19 databases is predicted via machine learning (ML). Using only 7 structural features …

[HTML][HTML] Direct prediction of gas adsorption via spatial atom interaction learning

J Cui, F Wu, W Zhang, L Yang, J Hu, Y Fang… - Nature …, 2023 - nature.com
Physisorption relying on crystalline porous materials offers prospective avenues for
sustainable separation processes, greenhouse gas capture, and energy storage. However …