Machine learning accelerates the investigation of targeted MOFs: performance prediction, rational design and intelligent synthesis

J Lin, Z Liu, Y Guo, S Wang, Z Tao, X Xue, R Li, S Feng… - Nano Today, 2023 - Elsevier
Metal-organic frameworks (MOFs) are a new class of nanoporous materials that are widely
used in various emerging fields due to their large specific surface area, high porosity and …

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 …

Beyond the Status Quo: Density Functional Tight Binding and Neural Network Potentials as a Versatile Simulation Strategy to Characterize Host–Guest Interactions in …

TS Hofer, RV Listyarini, E Hajdarevic… - The Journal of …, 2023 - ACS Publications
In recent years, research focused on synthesis, characterization, and application of metal–
organic frameworks (MOFs) has attracted increased interest, from both an experimental as …

Do Residual Solvent Molecules Always Hinder Gas Sorption in Metal–Organic Frameworks?

I Cooley, E Besley - Chemistry of Materials, 2023 - ACS Publications
The nature and magnitude of effects of residual solvent on gas uptake and selectivity in
metal–organic frameworks (MOFs) have been systematically studied using high-throughput …

A formally exact theory to construct nonreactive forcefields using linear regression to optimize bonded parameters

TA Manz - RSC advances, 2024 - pubs.rsc.org
This article derives theoretical foundations of force field functional theory (FFFT). FFFT
studies topics related to the functional representation of nonreactive forcefields to achieve …

An automated protocol to construct flexibility parameters for classical forcefields: applications to metal–organic frameworks

R Ghanavati, AC Escobosa, TA Manz - RSC advances, 2024 - pubs.rsc.org
In this work, forcefield flexibility parameters were constructed and validated for more than
100 metal–organic frameworks (MOFs). We used atom typing to identify bond types, angle …

Transferable and extensible machine learning-derived atomic charges for modeling hybrid nanoporous materials

VV Korolev, A Mitrofanov, EI Marchenko… - Chemistry of …, 2020 - ACS Publications
Nanoporous materials have attracted significant interest as an emerging platform for
adsorption-related applications. The high-throughput computational screening became a …

Identifying misbonded atoms in the 2019 CoRE metal–organic framework database

T Chen, TA Manz - RSC advances, 2020 - pubs.rsc.org
Databases of experimentally-derived metal–organic framework (MOF) crystal structures are
useful for large-scale computational screening to identify which MOFs are best-suited for …

Seven confluence principles: a case study of standardized statistical analysis for 26 methods that assign net atomic charges in molecules

TA Manz - RSC advances, 2020 - pubs.rsc.org
This article studies two kinds of information extracted from statistical correlations between
methods for assigning net atomic charges (NACs) in molecules. First, relative charge …

Efficient and accurate charge assignments via a multilayer connectivity-based atom contribution (m-CBAC) approach

C Zou, DR Penley, EH Cho, LC Lin - The Journal of Physical …, 2020 - ACS Publications
Metal–organic frameworks (MOFs) have drawn considerable attention for their potential in a
variety of energy applications such as gas separations and storage. With thousands of MOFs …