Isotope Effects in Liquid Water via Deep Potential Molecular Dynamics

Hsin-Yu Ko, Linfeng Zhang, Biswajit Santra, Han Wang, Weinan E, Robert A. DiStasio Jr., and Roberto Car
arXiv:1904.04930

Abstract

A comprehensive microscopic understanding of ambient liquid water is a major challenge for ab initio simulations as it simultaneously requires an accurate quantum mechanical description of the underlying potential energy surface (PES) as well as extensive sampling of configuration space. Due to the presence of light atoms (e.g., H or D), nuclear quantum fluctuations lead to observable changes in the structural properties of liquid water (e.g., isotope effects), and therefore provide yet another challenge for ab initio approaches. In this work, we demonstrate that the combination of dispersion-inclusive hybrid density functional theory (DFT), the Feynman discretized path-integral (PI) approach, and machine learning (ML) constitutes a versatile ab initio based framework that enables extensive sampling of both thermal and nuclear quantum fluctuations on a quite accurate underlying PES. In particular, we employ the recently developed deep potential molecular dynamics (DPMD) model—a neural-network representation of the ab initio PES—in conjunction with a PI approach based on the generalized Langevin equation (PIGLET) to investigate how isotope effects influence the structural properties of ambient liquid H2O and D2O. Through a detailed analysis of the interference differential cross sections as well as several radial and angular distribution functions, we demonstrate that this approach can furnish a semi-quantitative prediction of these subtle isotope effects.

URL: https://arxiv.org/abs/1904.04930.

Learning from the Density to Correct Total Energy and Forces in First Principle Simulations

Sebastian Dick and Marivi Fernandez-Serra
arXiv:1812.06572

Abstract
We propose a new molecular simulation framework that combines the transferability, robustness and chemical flexibility of an ab initio method with the accuracy and efficiency of a machine learned force field. The key to achieve this mix is to use a standard density functional theory (DFT) simulation as a pre-processor for the atomic and molecular information, obtaining a good quality electronic density. General, symmetry preserving, atom-centered electronic descriptors are then built from this density to train a neural network to correct the baseline DFT energies and forces. These electronic descriptors encode much more information than local atomic environments, allowing a simple neural network to reach the accuracy required for the problem of study at a negligible cost. The balance between accuracy and efficiency is determined by the baseline simulation. This is shown in results where high level quantum chemical accuracy is obtained for simulations of liquid water at standard DFT cost, or where high level DFT-accuracy is achieved in simulations with a low-level baseline DFT calculation, at a significantly reduced cost.

URL: https://arxiv.org/abs/1812.06572.

Hyperuniformity and Local Structures in Amorphous Ices

Fausto Martelli, Salvatore Torquato, Nicolas Giovambattista, and Roberto Car

Abstract
Water can exist in more than one glassy state, the most common forms of glassy water being low-density (LDA) and high-density (HDA) amorphous ice. LDA and HDA are remarkably different with a density difference of around 20−25%. In a recent investigation, we found that LDA and HDA are both nearly hyperuniform (Martelli et. al., Phys. Rev. Lett., 119, 136002 (2017)), implying that both amorphous ices are characterized by a significant suppression of large-scale density fluctuations. In this article, we inspect the connection between hyperuniformity in LDA and HDA, and the corresponding local tetrahedral order and underlying hydrogen bond network (HBN). We find that LDA and HDA are very different in terms of the local tetrahedral order and yet, both amorphous ices retain, on average, almost perfect 4-coordination. However, while the HBN of LDA has practically no defects (as in the case of the HBN of ice), the HBN of HDA has a non negligible fraction (∼10%) of defective sites corresponding to molecules having 3 or 5 HBs. This implies that hyperuniformity does not require the presence of a perfect (ice-like) HBN in the system. We also explore the compression-induced LDA-to-HDA and ice Ih-to-HDA first-order-like phase transitions. During both transformations, abrupt changes in the local structure of the system occur and the system becomes hyposurficial (devoid of ”surface-area fluctuations”) and non-hyperuniform. Surprisingly, the system retains on average almost perfect 4-coordination. It follows that glassy ices with 4-fold coordination are not necessarily hyperuniform. We conclude by showing that the loss of hyperuniformity and in particular, the appearance of hyposurficiality, during the ice Ih-to-HDA and LDA-to-HDA transformation is accompanied by enhanced density fluctuations and an anomalous increase in exchanges of HBs among the water molecules.

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