ArticleNature communications2019
Machine learning coarse grained models for water.
Article in Nature communications, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Analytical coarse grained potential parameterization by reinforcement learning for anisotropic cellulose.Journal of molecular modeling · 2026Article
- NNP/CG-MM: Embedding of All-Atom Neural Network Potentials into a Coarse-Grained Molecular Mechanics Environment.Journal of chemical theory and computation · 2026Article
- Deciphering the complexities of crystalline state(s) with molecular simulations.Communications chemistry · 2025Review
- Molecular-resolution imaging of ice crystallized from liquid water by cryogenic liquid-cell TEM.Nature communications · 2025Article
- Computational investigation of water glasses using machine-learning potentials.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Maximum Entropy-Mediated Liquid-to-Solid Nucleation and Transition.Journal of chemical theory and computation · 2025Article
- Genetic Algorithm Workflow for Parameterization of a Water Model Using the Vashishta Force Field.The journal of physical chemistry. B · 2025Article
- Prediction rigidities for data-driven chemistry.Faraday discussions · 2025Article
- Medium-density amorphous ice unveils shear rate as a new dimension in water's phase diagram.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Heat and mass transfer of water flow in graphene nanochannels: effect of pressure and interfacial interaction.RSC advances · 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
- Coarse-Graining with Equivariant Neural Networks: A Path Toward Accurate and Data-Efficient Models.The journal of physical chemistry. B · 2023Article
- Lessons Learned from Multiobjective Automatic Optimizations of Classical Three-Site Rigid Water Models Using Microscopic and Macroscopic Target Experimental Observables.Journal of chemical and engineering data · 2023Article
- Coarse-grainedChemical communications (Cambridge, England) · 2023Article
- Performance efficient macromolecular mechanics via sub-nanometer shape based coarse graining.Nature communications · 2023Article
- Microstructure of the fluid particles around the rigid body at the shear-thickening state toward understanding of the fluid mechanics.Scientific reports · 2021Article
- Advances in the study of supercooled water.The European physical journal. E, Soft matter · 2021Review
- Learning neural network potentials from experimental data via Differentiable Trajectory Reweighting.Nature communications · 2021Article
- "Dividing and Conquering" and "Caching" in Molecular Modeling.International journal of molecular sciences · 2021Review
- Bond order redefinition needed to reduce inherent noise in molecular dynamics simulations.Scientific reports · 2021Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
An accurate and computationally efficient molecular level description of mesoscopic behavior of ice-water systems remains a major challenge. Here, we introduce a set of machine-learned coarse-grained (CG) models (ML-BOP, ML-BOP
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