ArticleNature communications2023
Nuclear quantum effects on zeolite proton hopping kinetics explored with machine learning potentials and path integral molecular dynamics.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 63 citations in OpenAlex.
- Ultrafast bimolecular reaction catalysis via engineered nuclear quantum effects in helium nanodroplets.Nature communications · 2026Article
- Cold Chemistry Probes Isotope Effects in the BeThe journal of physical chemistry letters · 2026Article
- MixPI: Mixed-time slicing path integral software for quantized molecular dynamics simulations.The Journal of chemical physics · 2026Article
- Computational Modeling of Reticular Materials: The Past, the Present, and the Future.Advanced materials (Deerfield Beach, Fla.) · 2025Review
- Equivariant Neural Networks Reveal How Host-Guest Interactions ShapeThe journal of physical chemistry letters · 2025Article
- Interactions of Polar and Nonpolar Groups of Alcohols in Zeolite Pores.Journal of the American Chemical Society · 2025Article
- Nuclear quantum effects in molecular liquids across chemical space.Nature communications · 2025Article
- Probing nuclear quantum effects in electrocatalysis via a machine-learning enhanced grand canonical constant potential approach.Nature communications · 2025Article
- Investigating the Flexibility of H-ZSM-5 Zeolite Upon Adsorption of Coke Precursors: A Theoretical and Experimental Approach.The journal of physical chemistry. C, Nanomaterials and interfaces · 2025Article
- The Operando Nature of Isobutene Adsorbed in Zeolite H-SSZ-13 Unraveled by Machine Learning Potentials Beyond DFT Accuracy.Angewandte Chemie (International ed. in English) · 2025Article
- A reactive neural network framework for water-loaded acidic zeolites.Nature communications · 2024Article
- Machine learning potential for modelling HChemical science · 2024Article
- Continuum Robots and Magnetic Soft Robots: From Models to Interdisciplinary Challenges for Medical Applications.Micromachines · 2024Review
- OGRe: Optimal Grid Refinement Protocol for Accurate Free Energy Surfaces and Its Application in Proton Hopping in Zeolites and 2D COF Stacking.Journal of chemical theory and computation · 2023Article
- Effect of Framework Composition and NHACS central science · 2023Article
- Operando Modeling of Zeolite-Catalyzed Reactions Using First-Principles Molecular Dynamics Simulations.ACS catalysis · 2023Review
- Challenges in modelling dynamic processes in realistic nanostructured materials at operating conditions.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2023Review
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Proton hopping is a key reactive process within zeolite catalysis. However, the accurate determination of its kinetics poses major challenges both for theoreticians and experimentalists. Nuclear quantum effects (NQEs) are known to influence the structure and dynamics of protons, but their rigorous inclusion through the path integral molecular dynamics (PIMD) formalism was so far beyond reach for zeolite catalyzed processes due to the excessive computational cost of evaluating all forces and energies at the Density Functional Theory (DFT) level. Herein, we overcome this limitation by training first a reactive machine learning potential (MLP) that can reproduce with high fidelity the DFT potential energy surface of proton hopping around the first Al coordination sphere in the H-CHA zeolite. The MLP offers an immense computational speedup, enabling us to derive accurate reaction kinetics beyond standard transition state theory for the proton hopping reaction. Overall, more than 0.6 μs of simulation time was needed, which is far beyond reach of any standard DFT approach. NQEs are found to significantly impact the proton hopping kinetics up to ~473 K. Moreover, PIMD simulations with deuterium can be performed without any additional training to compute kinetic isotope effects over a broad range of temperatures.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.