ArticleProceedings of the National Academy of Sciences of the United States of America2021
Machine learning potentials for complex aqueous systems made simple.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers.
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54 citing papers in PubMed.
- GMUPC: a new package for global-minimum search of large uranyl peroxide clusters with machine learning potentials and DFT calculations.Journal of molecular modeling · 2026Article
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- Prediction and Experimental Verification of Electrolyte Solvation Structure from an OMol25-Trained Interatomic Potential.The journal of physical chemistry letters · 2026Article
- 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
- Understanding Adsorption and Reactions at Aqueous Oxide Interfaces with Neural Network Potential Molecular Dynamics.Accounts of materials research · 2026Article
- More Converged, Less Accurate? Reassessing Standard Choices for Ab Initio Water Using Machine Learning Potentials.The journal of physical chemistry. B · 2026Article
- Short-Range Machine-Learning Potentials for Aqueous Electrolyte Solutions.Chemphyschem : a European journal of chemical physics and physical chemistry · 2026Article
- Nanoconfined superionic water is a molecular superionic.Science advances · 2026Article
- Transition from Vehicular to Structural Ionic Transport in Electrified Alkali Aqueous Solutions.The journal of physical chemistry. B · 2026Article
- Clay Edges Are Dynamic Proton-Conducting Networks Modulated by Structure and pH.The journal of physical chemistry letters · 2026Article
- The CP2K Program Package Made Simple.The journal of physical chemistry. B · 2026Article
- Towards Routine Condensed Phase Simulations with Delta-Learned Coupled Cluster Accuracy: Application to Liquid Water.Journal of chemical theory and computation · 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
- Weighted active space protocol for multireference machine-learned potentials.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- COProceedings of the National Academy of Sciences of the United States of America · 2025Article
- Tuning water dissociation at oxide-electrolyte interfaces with electric fields.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Artificial Intelligence Paradigms for Next-Generation Metal-Organic Framework Research.Journal of the American Chemical Society · 2025Review
- Surface melting-driven hydrogen absorption for high-pressure polyhydride synthesis.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- MACE-OFF: Short-Range Transferable Machine Learning Force Fields for Organic Molecules.Journal of the American Chemical Society · 2025Article
- Protons Accumulate at the Graphene-Water Interface.ACS nano · 2025Article
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6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Simulation techniques based on accurate and efficient representations of potential energy surfaces are urgently needed for the understanding of complex systems such as solid-liquid interfaces. Here we present a machine learning framework that enables the efficient development and validation of models for complex aqueous systems. Instead of trying to deliver a globally optimal machine learning potential, we propose to develop models applicable to specific thermodynamic state points in a simple and user-friendly process. After an initial ab initio simulation, a machine learning potential is constructed with minimum human effort through a data-driven active learning protocol. Such models can afterward be applied in exhaustive simulations to provide reliable answers for the scientific question at hand or to systematically explore the thermal performance of ab initio methods. We showcase this methodology on a diverse set of aqueous systems comprising bulk water with different ions in solution, water on a titanium dioxide surface, and water confined in nanotubes and between molybdenum disulfide sheets. Highlighting the accuracy of our approach with respect to the underlying ab initio reference, the resulting models are evaluated in detail with an automated validation protocol that includes structural and dynamical properties and the precision of the force prediction of the models. Finally, we demonstrate the capabilities of our approach for the description of water on the rutile titanium dioxide (110) surface to analyze the structure and mobility of water on this surface. Such machine learning models provide a straightforward and uncomplicated but accurate extension of simulation time and length scales for complex systems.
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