ArticleNature communications2024
Machine learning the electric field response of condensed phase systems using perturbed neural network potentials.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Using structure to evaluate the accuracy and transferability of machine learning potentials of biomolecules.RSC advances · 2026Article
- Simultaneous learning of static and dynamic charges.Physical chemistry chemical physics : PCCP · 2026Article
- Learning Electronic Polarization in Molecular Systems: Vibrational Spectroscopy of Ethanol-Water Mixtures.Journal of chemical information and modeling · 2026Article
- Simulating enzyme catalysis with electrostatically embedded machine learning potentials.Chemical science · 2026Article
- Transition from Vehicular to Structural Ionic Transport in Electrified Alkali Aqueous Solutions.The journal of physical chemistry. B · 2026Article
- Long-range electrostatics for machine learning interatomic potentials is easier than we thought.The Journal of chemical physics · 2026Article
- Entropy Governs the Structure and Reactivity of Water Dissociation Under Electric Fields.Journal of the American Chemical Society · 2025Article
- Carbon Dioxide Electroreduction on Gold without Metal or Organic Cations.ACS catalysis · 2025Article
- A facile approach for generating ordered oxygen vacancies in metal oxides.Nature materials · 2025Article
- Unified differentiable learning of electric response.Nature communications · 2025Article
- The Impact of Electric Fields on Processes at Electrode Interfaces.Chemical reviews · 2025Review
- PiNN: Equivariant Neural Network Suite for Modeling Electrochemical Systems.Journal of chemical theory and computation · 2025Article
- Mechanism of Fe(II) Chemisorption on Hematite(001) Revealed by Reactive Neural Network Potential Molecular Dynamics.The journal of physical chemistry letters · 2025Article
- Electric-field driven nuclear dynamics of liquids and solids from a multi-valued machine-learned dipolar model.npj computational materials · 2025Article
- Machine learning the electric field response of condensed phase systems using perturbed neural network potentials.Nature communications · 2024Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The interaction of condensed phase systems with external electric fields is of major importance in a myriad of processes in nature and technology, ranging from the field-directed motion of cells (galvanotaxis), to geochemistry and the formation of ice phases on planets, to field-directed chemical catalysis and energy storage and conversion systems including supercapacitors, batteries and solar cells. Molecular simulation in the presence of electric fields would give important atomistic insight into these processes but applications of the most accurate methods such as ab-initio molecular dynamics (AIMD) are limited in scope by their computational expense. Here we introduce Perturbed Neural Network Potential Molecular Dynamics (PNNP MD) to push back the accessible time and length scales of such simulations. We demonstrate that important dielectric properties of liquid water including the field-induced relaxation dynamics, the dielectric constant and the field-dependent IR spectrum can be machine learned up to surprisingly high field strengths of about 0.2 V Å
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