ArticleJournal of chemical theory and computation2024
Capturing Dichotomic Solvent Behavior in Solute-Solvent Reactions with Neural Network Potentials.
Article in Journal of chemical theory and computation, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 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
- Active learning meets metadynamics: automated workflow for reactive machine learning interatomic potentials.Digital discovery · 2026Article
- Automated Microsolvation for Minimum Energy Path Construction in Solution.Journal of chemical theory and computation · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Simulations of chemical reactivity in condensed phase systems represent an ongoing challenge in computational chemistry, where traditional quantum chemical approaches typically struggle with both the size of the system and the potential complexity of the reaction. Here, we introduce a workflow aimed at efficiently training neural network potentials (NNPs) to explore energy barriers in solution at the hybrid density functional theory level. The computational burden associated with training at the PBE0-D3(BJ) level is bypassed through the use of active and transfer learning techniques, whereas extensive sampling of the transition state region is accelerated by well-tempered metadynamics simulations using multiple time step integration. These NNPs serve to explore a puzzling solute-solvent reactivity route involving the ring opening of
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Registered trials
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