ArticleJournal of chemical theory and computation2023
PiNNwall: Heterogeneous Electrode Models from Integrating Machine Learning and Atomistic Simulation.
Article in Journal of chemical theory and computation, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 20 citations in OpenAlex.
- Simultaneous learning of static and dynamic charges.Physical chemistry chemical physics : PCCP · 2026Article
- Article
- Carbon Dioxide Electroreduction on Gold without Metal or Organic Cations.ACS catalysis · 2025Article
- Mastering Proton Activities in Aqueous Batteries.Advanced materials (Deerfield Beach, Fla.) · 2025Article
- PiNN: Equivariant Neural Network Suite for Modeling Electrochemical Systems.Journal of chemical theory and computation · 2025Article
- Review
- Impedance of nanocapacitors from molecular simulations to understand the dynamics of confined electrolytes.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Understanding Sorption of Aqueous Electrolytes in Porous Carbon by NMR Spectroscopy.Journal of the American Chemical Society · 2024Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Electrochemical energy storage always involves the capacitive process. The prevailing electrode model used in the molecular simulation of polarizable electrode-electrolyte systems is the Siepmann-Sprik model developed for perfect metal electrodes. This model has been recently extended to study the metallicity in the electrode by including the Thomas-Fermi screening length. Nevertheless, a further extension to heterogeneous electrode models requires introducing chemical specificity, which does not have any analytical recipes. Here, we address this challenge by integrating the atomistic machine learning code (PiNN) for generating the base charge and response kernel and the classical molecular dynamics code (MetalWalls) dedicated to the modeling of electrochemical systems, and this leads to the development of the PiNNwall interface. Apart from the cases of chemically doped graphene and graphene oxide electrodes as shown in this study, the PiNNwall interface also allows us to probe polarized oxide surfaces in which both the proton charge and the electronic charge can coexist. Therefore, this work opens the door for modeling heterogeneous and complex electrode materials often found in energy storage systems.
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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.