Construction of high accuracy machine learning interatomic potential for surface/interface of nanomaterials—A review
The inherent discontinuity and unique dimensional attributes of nanomaterial surfaces and
interfaces bestow them with various exceptional properties. These properties, however, also …
interfaces bestow them with various exceptional properties. These properties, however, also …
Operando modeling of zeolite-catalyzed reactions using first-principles molecular dynamics simulations
Within this Perspective, we critically reflect on the role of first-principles molecular dynamics
(MD) simulations in unraveling the catalytic function within zeolites under operating …
(MD) simulations in unraveling the catalytic function within zeolites under operating …
A reactive neural network framework for water-loaded acidic zeolites
Under operating conditions, the dynamics of water and ions confined within protonic
aluminosilicate zeolite micropores are responsible for many of their properties, including …
aluminosilicate zeolite micropores are responsible for many of their properties, including …
Fast prediction of anharmonic vibrational spectra for complex organic molecules
M Miotto, L Monacelli - npj Computational Materials, 2024 - nature.com
Interpreting Raman and IR vibrational spectra in complex organic molecules lacking
symmetries poses a formidable challenge. In this study, we propose an innovative approach …
symmetries poses a formidable challenge. In this study, we propose an innovative approach …
OGRe: optimal grid refinement protocol for accurate free energy surfaces and its application in proton hopping in zeolites and 2D COF stacking
While free energy surfaces are the crux of our understanding of many chemical and
biological processes, their accuracy is generally unknown. Moreover, many developments to …
biological processes, their accuracy is generally unknown. Moreover, many developments to …
Machine learning interatomic potentials for heterogeneous catalysis
Atomistic modeling can provide valuable insights into the design of novel heterogeneous
catalysts as needed nowadays in the areas of, eg, chemistry, materials science, and biology …
catalysts as needed nowadays in the areas of, eg, chemistry, materials science, and biology …
Machine Learning Potentials for Heterogeneous Catalysis
The production of many bulk chemicals relies on heterogeneous catalysis. The rational
design or improvement of the required catalysts critically depends on insights into the …
design or improvement of the required catalysts critically depends on insights into the …
Challenges in modelling dynamic processes in realistic nanostructured materials at operating conditions
V Van Speybroeck - … Transactions of the Royal Society A, 2023 - royalsocietypublishing.org
The question is addressed in how far current modelling strategies are capable of modelling
dynamic phenomena in realistic nanostructured materials at operating conditions …
dynamic phenomena in realistic nanostructured materials at operating conditions …
Effect of Framework Composition and NH3 on the Diffusion of Cu+ in Cu-CHA Catalysts Predicted by Machine-Learning Accelerated Molecular Dynamics
R Millan, E Bello-Jurado, M Moliner… - ACS Central …, 2023 - ACS Publications
Cu-exchanged zeolites rely on mobile solvated Cu+ cations for their catalytic activity, but the
role of the framework composition in transport is not fully understood. Ab initio molecular …
role of the framework composition in transport is not fully understood. Ab initio molecular …
Machine learning potential for modelling H 2 adsorption/diffusion in MOFs with open metal sites
Metal–organic frameworks (MOFs) incorporating open metal sites (OMS) have been
identified as promising sorbents for many societally relevant-adsorption applications …
identified as promising sorbents for many societally relevant-adsorption applications …