Signatures of a liquid–liquid transition in an ab initio deep neural network model for water

Journal Article · Fri Oct 02 00:00:00 EDT 2020 · Proceedings of the National Academy of Sciences of the United States of America

DOI:https://doi.org/10.1073/pnas.2015440117· OSTI ID:1670208

Gartner, III, Thomas E.  ^[1]; Zhang, Linfeng  ^[2]; Piaggi, Pablo M.  ^[1]; Car, Roberto  ^[3]; Panagiotopoulos, Athanassios Z.  ^[4]; Debenedetti, Pablo G.  ^[5]

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1. Department of Chemistry, Princeton University, Princeton, NJ 08544,
2. Program in Applied and Computational Mathematics, Princeton University, Princeton, NJ 08544,
3. Department of Chemistry, Princeton University, Princeton, NJ 08544,, Program in Applied and Computational Mathematics, Princeton University, Princeton, NJ 08544,, Department of Physics, Princeton University, Princeton, NJ 08544,, Princeton Institute for the Science and Technology of Materials, Princeton University, Princeton, NJ 08544,
4. Princeton Institute for the Science and Technology of Materials, Princeton University, Princeton, NJ 08544,, Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ 08544
5. Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ 08544

The possible existence of a metastable liquid–liquid transition (LLT) and a corresponding liquid–liquid critical point (LLCP) in supercooled liquid water remains a topic of much debate. An LLT has been rigorously proved in three empirically parametrized molecular models of water, and evidence consistent with an LLT has been reported for several other such models. In contrast, experimental proof of this phenomenon has been elusive due to rapid ice nucleation under deeply supercooled conditions. In this work, we combined density functional theory (DFT), machine learning, and molecular simulations to shed additional light on the possible existence of an LLT in water. We trained a deep neural network (DNN) model to represent the ab initio potential energy surface of water from DFT calculations using the Strongly Constrained and Appropriately Normed (SCAN) functional. We then used advanced sampling simulations in the multithermal–multibaric ensemble to efficiently explore the thermophysical properties of the DNN model. The simulation results are consistent with the existence of an LLCP, although they do not constitute a rigorous proof thereof. We fit the simulation data to a two-state equation of state to provide an estimate of the LLCP’s location. These combined results—obtained from a purely first-principles approach with no empirical parameters—are strongly suggestive of the existence of an LLT, bolstering the hypothesis that water can separate into two distinct liquid forms.

• Research Organization: Princeton Univ., NJ (United States)
• Sponsoring Organization: USDOE Office of Science (SC)
• Grant/Contract Number: SC001934; SC0019394
• OSTI ID: 1670208
• Alternate ID(s): OSTI ID: 1853194; OSTI ID: 1999129
• Journal Information: Proceedings of the National Academy of Sciences of the United States of America, Journal Name: Proceedings of the National Academy of Sciences of the United States of America Vol. 117 Journal Issue: 42; ISSN 0027-8424
• Publisher: Proceedings of the National Academy of Sciences
• Country of Publication: United States
• Language: English

Related Subjects

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY
97 MATHEMATICS AND COMPUTING
71 CLASSICAL AND QUANTUM MECHANICS
GENERAL PHYSICS
36 MATERIALS SCIENCE
Science & Technology - Other Topics
water
liquid–liquid transition
molecular simulations
machine learning