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Density functional theory of waterwith the machine-learned DM21 functional
Etienne Palos1, Eleftherios Lambros1, Saswata Dasgupta1
1Department of Chemistry and Biochemistry, University of California San Diego, La Jolla, California 92093, USA.
The Journal of Chemical Physics
|April 30, 2022
Summary
The new DeepMind 21 (DM21) functional shows promise for describing water but has size-dependent errors. These errors lead to an overestimation of hydrogen-bond strength in liquid water simulations.
Area of Science:
- Computational Chemistry
- Materials Science
- Quantum Mechanics
Background:
- Density Functional Theory (DFT) struggles to accurately model water due to functional-driven and density-driven errors.
- Traditional Density Functional Approximations (DFAs) have limitations in describing aqueous systems.
Purpose of the Study:
- To evaluate the DeepMind 21 (DM21) functional for describing water clusters and liquid water properties.
- To assess if DM21 can overcome limitations of traditional DFAs for aqueous systems.
Main Methods:
- Assessed DM21 functional accuracy for neutral, protonated, and deprotonated water clusters.
- Developed the many-body MB-DM21 potential from DM21 data.
- Performed simulations of liquid water using MB-DM21 at varying temperatures.
Main Results:
- DM21's accuracy in predicting water cluster energetics varies with cluster size.
- The MB-DM21 potential overestimates hydrogen-bond strength in liquid water.
- Simulations with MB-DM21 predict a more ice-like structure for water at room temperature.
Conclusions:
- Size-dependent errors in DM21 impact its performance for aqueous systems.
- The MB-DM21 potential, while derived from an advanced functional, still exhibits limitations in describing liquid water's structure and bonding.
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