ArticleProceedings of the National Academy of Sciences of the United States of America2020
Signatures of a liquid-liquid transition in an ab initio deep neural network model for water.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
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38 citing papers in PubMed.
- Computational Methods for Molecular Dynamics of Supercooled Water Between 200 and 273 K.Entropy (Basel, Switzerland) · 2026Review
- Formation of nanoscale vapour films governing thermal resistance in amorphous ice.Communications chemistry · 2026Article
- Nuclear Quantum Effects Reshape Structural Signatures of Supercooled Water near the Liquid-Liquid Critical Region.The journal of physical chemistry. B · 2026Article
- Prediction and Experimental Verification of Electrolyte Solvation Structure from an OMol25-Trained Interatomic Potential.The journal of physical chemistry letters · 2026Article
- Liquid anomalies and fragility of supercooled antimony.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Direct observation of liquid-liquid phase coexistence in deeply supercooled water using an accurate polarizable multipole model.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Absence of higher than sixfold coordination in glassy GeOProceedings of the National Academy of Sciences of the United States of America · 2025Article
- A literature-derived dataset of migration barriers for quantifying ionic transport in battery materials.Scientific data · 2025Article
- Ab initio machine-learning simulation of calcium carbonate from aqueous solutions to the solid state.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Computational investigation of water glasses using machine-learning potentials.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Nuclear quantum effects on glassy water under pressure: Vitrification and pressure-induced transformations.The Journal of chemical physics · 2024Article
- Supercritical density fluctuations and structural heterogeneity in supercooled water-glycerol microdroplets.Nature communications · 2024Article
- Evidence of ferroelectric features in low-density supercooled water from ab initio deep neural-network simulations.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Liquid-liquid transition and ice crystallization in a machine-learned coarse-grained water model.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Potential energy landscape of a flexible water model: Equation of state, configurational entropy, and Adam-Gibbs relationship.The Journal of chemical physics · 2024Article
- Water Potential from Adaptive Force Matching for Ice and Liquid with Revised Dispersion Predicts Supercooled Liquid Anomalies in Good Agreement with Two Independent Experimental Fits.The journal of physical chemistry. B · 2024Article
- Probing the self-ionization of liquid water with ab initio deep potential molecular dynamics.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- DeePMD-kit v2: A software package for deep potential models.The Journal of chemical physics · 2023Article
- Realistic phase diagram of water from "first principles" data-driven quantum simulations.Nature communications · 2023Article
- Are Neural Network Potentials Trained on Liquid States Transferable to Crystal Nucleation? A Test on Ice Nucleation in the mW Water Model.The journal of physical chemistry. B · 2023Article
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6 authors.
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Abstract
The possible existence of a metastable liquid-liquid transition (LLT) and a corresponding liquid-liquid critical point (LLCP) in supercooled liquid water remains a topic of much debate. An LLT has been rigorously proved in three empirically parametrized molecular models of water, and evidence consistent with an LLT has been reported for several other such models. In contrast, experimental proof of this phenomenon has been elusive due to rapid ice nucleation under deeply supercooled conditions. In this work, we combined density functional theory (DFT), machine learning, and molecular simulations to shed additional light on the possible existence of an LLT in water. We trained a deep neural network (DNN) model to represent the ab initio potential energy surface of water from DFT calculations using the Strongly Constrained and Appropriately Normed (SCAN) functional. We then used advanced sampling simulations in the multithermal-multibaric ensemble to efficiently explore the thermophysical properties of the DNN model. The simulation results are consistent with the existence of an LLCP, although they do not constitute a rigorous proof thereof. We fit the simulation data to a two-state equation of state to provide an estimate of the LLCP's location. These combined results-obtained from a purely first-principles approach with no empirical parameters-are strongly suggestive of the existence of an LLT, bolstering the hypothesis that water can separate into two distinct liquid forms.
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