ArticleChemical science2023
Accelerating explicit solvent models of heterogeneous catalysts with machine learning interatomic potentials.
Article in Chemical science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- The Future of Foundation Machine Learning Potentials and DFT in Homogeneous Catalysis: Competition or Synergy?Chemistry (Weinheim an der Bergstrasse, Germany) · 2026Article
- Altered morphology and diffusivity of water confined in MXenes: Machine learning-accelerated computations combined with experiments.Science advances · 2026Article
- Optimizing cross-domain transfer for universal machine learning interatomic potentials.Nature communications · 2026Article
- Methodological Frameworks for Computational Electrocatalysis: From Theory to Practice.Small methods · 2026Review
- Excited-state nonadiabatic dynamics in explicit solvent using machine learned interatomic potentials.Digital discovery · 2025Article
- A grand canonical study of the potential dependence of nitrate adsorption and dissociation across metals and dilute alloys.Communications chemistry · 2025Article
- Analytical First Derivatives of the SCF Energy for the Conductor-Like Polarizable Continuum Model With Non-Static Radii.Journal of computational chemistry · 2025Article
- A solution for 4-propylguaiacol hydrodeoxygenation without ring saturation.Nature communications · 2024Article
- Unveiling Competitive Adsorption in TiOACS applied materials & interfaces · 2024Article
- Origin of copper as a unique catalyst for C-C coupling in electrocatalytic COChemical science · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Realistically modelling how solvents affect catalytic reactions is a longstanding challenge due to its prohibitive computational cost. Typically, an explicit atomistic treatment of the solvent molecules is needed together with molecular dynamics (MD) simulations and enhanced sampling methods. Here, we demonstrate the utility of machine learning interatomic potentials (MLIPs), coupled with active learning, to enable fast and accurate explicit solvent modelling of adsorption and reactions on heterogeneous catalysts. MLIPs trained on-the-fly were able to accelerate
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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.