Machine learning accelerated interfacial fluxionality in Ni-supported metal nitride ammonia synthesis catalysts DOI
Pranav Roy, Brandon C. Bukowski

Journal of Catalysis, Journal Year: 2025, Volume and Issue: unknown, P. 116224 - 116224

Published: May 1, 2025

Language: Английский

A reactive neural network framework for water-loaded acidic zeolites DOI Creative Commons
Andreas Erlebach, Martin Šípka, Indranil Saha

et al.

Nature Communications, Journal Year: 2024, Volume and Issue: 15(1)

Published: May 17, 2024

Language: Английский

Citations

20
Chemically accurate predictions for water adsorption on Brønsted sites of zeolite H-MFI DOI Creative Commons
Henning Windeck, Fabian Berger, Joachim Sauer

et al.

Physical Chemistry Chemical Physics, Journal Year: 2024, Volume and Issue: 26(36), P. 23588 - 23599

Published: Jan. 1, 2024

Accurate predictions of the heat water adsorption and protonation state requires passing from density functional theory (PBE+D) to wavefunction methods (MP2).

Language: Английский

Citations

4
Learning descriptors to predict organic structure-directing agent applicability in zeolite synthesis DOI
Alexander J. Hoffman, Mingrou Xie, Rafael Gómez‐Bombarelli

et al.

Microporous and Mesoporous Materials, Journal Year: 2025, Volume and Issue: unknown, P. 113467 - 113467

Published: Jan. 1, 2025

Language: Английский

Citations

0
Machine Learning Accelerated Interfacial Fluxionality in Ni-Supported Metal Nitride Ammonia Synthesis Catalysts DOI
Pranav Roy, Brandon C. Bukowski

Published: Jan. 1, 2025

Language: Английский

Citations

0
Application of Machine Learning Interatomic Potentials in Heterogeneous Catalysis DOI

Gbolagade Olajide,

Khagendra Baral, Sophia Ezendu

et al.

Published: Jan. 1, 2025

Language: Английский

Citations

0
Zeolite–Water Chemistry: Characterization Methods to Unveil Zeolite Structure DOI Open Access
José Almeida,

Lu Song,

Sohrab Askarli

et al.

Chemistry - Methods, Journal Year: 2025, Volume and Issue: unknown

Published: March 24, 2025

Abstract This review provides comprehensive aspects of the interaction water with zeolites, focusing on its influence structural and catalytic properties zeolites. It details how can alter zeolite acidity by forming hydrogen bonding or hydronium ions through different modes in topologies. Moreover, it summarizes risks stability loss via hydrolysis Si−O−T bonds to stability, structure, reactivity To address interference, various strategies for removal from frameworks are reviewed proposed perspective By combining advanced in‐situ techniques, FTIR solid‐state NMR have proven effective providing atomic‐level insights, as they eliminate masking effects enable precise characterization framework. work underscores importance these methods minimizing water, enhancing reliability applications, insights into recent advancements, challenges, future directions related fields.

Language: Английский

Citations

0
Application of machine learning interatomic potentials in heterogeneous catalysis DOI

Gbolagade Olajide,

Khagendra Baral, Sophia Ezendu

et al.

Journal of Catalysis, Journal Year: 2025, Volume and Issue: unknown, P. 116202 - 116202

Published: May 1, 2025

Language: Английский

Citations

0
Machine learning accelerated interfacial fluxionality in Ni-supported metal nitride ammonia synthesis catalysts DOI
Pranav Roy, Brandon C. Bukowski

Journal of Catalysis, Journal Year: 2025, Volume and Issue: unknown, P. 116224 - 116224

Published: May 1, 2025

Language: Английский

Citations

0