Bulletin of the American Physical Society
2024 APS March Meeting
Monday–Friday, March 4–8, 2024; Minneapolis & Virtual
Session D60: Machine Learning of Molecules and Materials: Chemical Space and Dynamics
3:00 PM–5:48 PM,
Monday, March 4, 2024
Room: 207AB
Sponsoring
Unit:
DCOMP
Chair: Davide Tisi, Federal Institute of Technology (EPFL)
Abstract: D60.00003 : Thermodynamic and electronic properties of water and ice: joining machine learning and manybody perturbation theory*
3:48 PM–4:00 PM
Presenter:
Davide Donadio
(University of California Davis)
Authors:
Davide Donadio
(University of California Davis)
Margaret Berrens
(University of California Davis)
Arpan Kundu
(University of Chicago)
Zekun Chen
(University of California Davis)
Marcos Calegari Andrade
(Lawrence Livermore National Lab)
Tuan Anh Pham
(Lawrence Livermore Natl Lab)
Giulia Galli
(University of Chicago)
Modeling through accurate computer simulations is essential to connect experimental observation with the molecular-level picture of water and ice. It is by now established that nuclear quantum effects (NQEs) play a crucial role in determining the structural and thermodynamic properties of water. However, to date the understanding of how NQEs influence the electronic structure of water systems is limited, in particular in bulk ice, and in interfacial and confined water and ice systems.
Here, we conducted a systematic comparison of both the structural and electronic properties for various
water systems, including water clusters, liquid water, and hexagonal ice simulated at the level of classical molecular dynamics (MD) and quantum path-integral MD. These simulations leverage the efficiency and accuracy of machine learning potentials fitted to state-of-the-art density functional theory calculations. Electronic structure calculations using GW manybody perturbation theory provide a detailed insight of the distinct impact that NQEs have on different aqueous systems.
*This work is supported by the National Science Foundation under Grant No. 2053235.
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