Articlenpj computational materials2025
Electric-field driven nuclear dynamics of liquids and solids from a multi-valued machine-learned dipolar model.
Article in npj computational materials, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- 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
- 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
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Authors and funding
3 authors.
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Abstract
The driving of vibrational motion by external electric fields is a topic of continued interest, due to the possibility of assessing new or metastable material phases with desirable properties. Here, we combine ab initio molecular dynamics within the electric-dipole approximation with machine-learning neural networks (NNs) to develop a general, efficient and accurate method to perform electric-field-driven nuclear dynamics for molecules, solids, and liquids. We train equivariant and autodifferentiable NNs for the interatomic potential and the dipole, modifying the model infrastructure to account for the multi-valued nature of the latter in periodic systems. We showcase the method by addressing property modifications induced by electric field interactions in a polar liquid and a polar solid from nanosecond-long molecular dynamics simulations with quantum-mechanical accuracy. For liquid water, we present a calculation of the dielectric function in the GHz to THz range and the electrofreezing transition, showing that nuclear quantum effects enhance this phenomenon. For the ferroelectric perovskite LiNbO
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