ArticleProceedings of the National Academy of Sciences of the United States of America2023
Probing the self-ionization of liquid water with ab initio deep potential molecular dynamics.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- NNP/CG-MM: Embedding of All-Atom Neural Network Potentials into a Coarse-Grained Molecular Mechanics Environment.Journal of chemical theory and computation · 2026Article
- Understanding Adsorption and Reactions at Aqueous Oxide Interfaces with Neural Network Potential Molecular Dynamics.Accounts of materials research · 2026Article
- How reactive is water at the nanoscale and how to control it?Science advances · 2026Article
- Pulsed Dynamic Water Electrolysis: Mass Transfer Enhancement, Microenvironment Regulation, and Hydrogen Production Optimization.Nano-micro letters · 2026Review
- Entropy Governs the Structure and Reactivity of Water Dissociation Under Electric Fields.Journal of the American Chemical Society · 2025Article
- Computational investigation of water glasses using machine-learning potentials.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Thermal Degradation of Carboxymethyl Cellulose (CMC) in Saline Solution for Applications in Petroleum Industry Fluids.Polymers · 2025Article
- Protons Accumulate at the Graphene-Water Interface.ACS nano · 2025Article
- Spatiotemporal characterization of water diffusion anomalies in saline solutions using machine learning force field.Science advances · 2024Article
- Molecular-scale insights into the electrical double layer at oxide-electrolyte interfaces.Nature communications · 2024Article
- Machine learning demonstrates the impact of proton transfer and solvent dynamics on COChemical science · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
The chemical equilibrium between self-ionized and molecular water dictates the acid-base chemistry in aqueous solutions, yet understanding the microscopic mechanisms of water self-ionization remains experimentally and computationally challenging. Herein, Density Functional Theory (DFT)-based deep neural network (DNN) potentials are combined with enhanced sampling techniques and a global acid-base collective variable to perform extensive atomistic simulations of water self-ionization for model systems of increasing size. The explicit inclusion of long-range electrostatic interactions in the DNN potential is found to be crucial to accurately reproduce the DFT free energy profile of solvated water ion pairs in small (64 and 128 H
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