This award supports research and educational activities with an aim to advance our fundamental understanding of the physical and chemical properties of salt water. Salt water is ubiquitous on earth, and it plays important roles in many physiological and biological processes, such as signal transduction in living cells as well as protein solubility and folding. The precise picture of how water molecules arrange themselves and move around salt ions is prerequisite to understanding the properties of salt water. This requires a quantum mechanical modeling for both the electrons and nuclei of water and salt molecules. However, due to the high computational cost, current computational studies can only model salt water in small simulation boxes for short time periods, which makes it difficult to extract accurate information about the long-range effects of salt molecules on the molecular and electronic structures of water.
This project seeks to build theoretical tools and a computational framework to solve this problem by using machine learning methods. Utilizing computationally intensive calculations for only a few configurations of salt water, the PI and his team will build efficient deep learning models that can model salt water in simulation boxes containing thousands of water molecules for long time scales. The resulting deep learning models will not only be computationally efficient, but they will also be able to yield accurate predictions with comparable accuracy to advanced, fully quantum mechanical calculations. Both the microscopic and macroscopic properties of salt water will be accurately predicted by this approach and compared with experimental data.
The participating postdocs and graduate students will carry out cutting-edge, interdisciplinary research based on advanced molecular dynamics simulations and machine learning techniques in the fields of condensed matter theory and quantum chemistry. Accurate deep learning models of salt solutions will be developed and distributed to the computational materials community. An outreach activity that explores the science of water will be developed and presented to high school students in New Jersey.
This award supports research and educational activities that are aimed at understanding the hydration structure of important salt aqueous solutions and exploring how the hydrated ions will affect the hydrogen-bond network of liquid water. Ab initio molecular dynamics simulations based on density functional theory provide an ideal framework to study salt water from first principles. However, accurate predictions of salt water properties require a high-level functional approximation, and nuclear quantum effect should be taken into account due to the small mass of the proton. Accordingly, accurate first-principles calculations of salt water are computationally very expensive and cannot be applied to study the long-range effect of hydrated ions on the water structure.
This PI and his team will overcome these computational challenges by combined deep learning techniques and advanced density functional theory calculations. Efficient deep molecular dynamics models of salt solutions will be built on Feynman path integral ab initio molecular dynamics simulations based on the hybrid meta-GGA SCAN0 density functional. Using deep learning models, molecular dynamics simulations of salt water can be performed in a simulation box containing thousands of water molecules at the nanosecond time scale, with a level of accuracy comparable to direct density functional theory calculations. With this powerful approach, the converged thermodynamic properties such as diffusivities and molecular structure in salt water can be accurately predicted and studied. Using the molecular trajectories of salt water, the electronic properties will also be modeled by deep learning techniques and compared to available experimental results.
The participating postdocs and graduate students will carry out cutting-edge, interdisciplinary research based on advanced ab initio molecular dynamics simulations and machine learning techniques in the fields of condensed matter theory and quantum chemistry. Accurate deep learning models of salt solutions will be developed and distributed to the computational materials community. An outreach activity that explores the science of water will be developed and presented to high school students in New Jersey.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
