ArticleNature communications2021
Quantum-mechanical exploration of the phase diagram of water.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 citing papers in PubMed.
- A unified machine-learning framework for ab initio multiscale modeling of liquids.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- More Converged, Less Accurate? Reassessing Standard Choices for Ab Initio Water Using Machine Learning Potentials.The journal of physical chemistry. B · 2026Article
- A Novel NPT Thermodynamic Integration Scheme to Derive Rigorous Gibbs Free Energies for Crystalline Solids.Journal of chemical theory and computation · 2026Article
- Nuclear quantum effects amplify autoionization-driven superionic behaviour in nanoconfined monolayer water.Chemical science · 2026Article
- Active learning meets metadynamics: automated workflow for reactive machine learning interatomic potentials.Digital discovery · 2026Article
- Molecular Simulation of Hydrogen Systems: From Properties and Methods to Applications and Future Directions.Chemical reviews · 2025Review
- Towards Routine Condensed Phase Simulations with Delta-Learned Coupled Cluster Accuracy: Application to Liquid Water.Journal of chemical theory and computation · 2025Article
- Efficient Determination of Water/Ice Phase Diagram through Isenthalpic-Isobaric Molecular Dynamics Simulations.The journal of physical chemistry. B · 2025Article
- Deep potential-driven structure exploration of ice polymorphs.Innovation (Cambridge (Mass.)) · 2025Article
- Calculating a Phase Diagram of a Simple Water Model Using Unsupervised Machine Learning on Simulation Data.Journal of chemical theory and computation · 2025Article
- Origin of dielectric polarization suppression in confined water from first principles.Chemical science · 2024Article
- 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
- Benchmarking First-Principles Reaction Equilibrium Composition Prediction.Molecules (Basel, Switzerland) · 2023Article
- Thermodynamics of diamond formation from hydrocarbon mixtures in planets.Nature communications · 2023Article
- Accurate crystal structure of ice VI from X-ray diffraction with Hirshfeld atom refinement.IUCrJ · 2022Article
- Thermodynamics of high-pressure ice phases explored with atomistic simulations.Nature communications · 2022Article
- Connection between water's dynamical and structural properties: Insights from ab initio simulations.Proceedings of the National Academy of Sciences of the United States of America · 2022Article
- Physics-driven coarse-grained model for biomolecular phase separation with near-quantitative accuracy.Nature computational science · 2021Article
- Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems.Chemical reviews · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
The set of known stable phases of water may not be complete, and some of the phase boundaries between them are fuzzy. Starting from liquid water and a comprehensive set of 50 ice structures, we compute the phase diagram at three hybrid density-functional-theory levels of approximation, accounting for thermal and nuclear fluctuations as well as proton disorder. Such calculations are only made tractable because we combine machine-learning methods and advanced free-energy techniques. The computed phase diagram is in qualitative agreement with experiment, particularly at pressures ≲ 8000 bar, and the discrepancy in chemical potential is comparable with the subtle uncertainties introduced by proton disorder and the spread between the three hybrid functionals. None of the hypothetical ice phases considered is thermodynamically stable in our calculations, suggesting the completeness of the experimental water phase diagram in the region considered. Our work demonstrates the feasibility of predicting the phase diagram of a polymorphic system from first principles and provides a thermodynamic way of testing the limits of quantum-mechanical calculations.
Identifiers
What OpenQuestion holds
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.