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Water is of the utmost importance for life and technology. However, a genuinely predictive ab initio model of water has eluded scientists. We demonstrate that a fully ab initio approach, relying on the strongly constrained and appropriately normed (SCAN) density functional, provides such a description of water. SCAN accurately describes the balance among covalent bonds, hydrogen bonds, and van der Waals interactions that dictates the structure and dynamics of liquid water. Notably, SCAN captures the density difference between water and ice Ih at ambient conditions, as well as many important structural, electronic, and dynamic properties of liquid water. These successful predictions of the versatile SCAN functional open the gates to study complex processes in aqueous phase chemistry and the interactions of water with other materials in an efficient, accurate, and predictive, ab initio manner.

Biological membranes are essential for cell life and hydration. Water provides the driving force for the assembly and stability of many cell components. Here, we study the structural properties of water in a phospholipid membrane. We characterize the local structures, inspecting the intermediate range order （IRO） and adopting a sensitive local order metric recently proposed by Martelli et al. that measures and grades the degree of overlap of the local environment with the structures of perfect ice. Close to the membrane, water acquires a high IRO and changes its dynamical properties; i.e., its translational and rotational degrees of freedom slow in a region that extends over - 1 nm from the membrane interface. Surprisingly, we show that at distances as far as -~ 2.5 mn from the interface, although the bulk-like dynamics are recovered, the IRO of water is still slightly higher than that in the bulk under the same thermodynamic conditions. Therefore, the water-membrane interface has a structural effect at ambient conditions that propagates further than the often-invoked 1-nm length scale. Consequently, this should be considered when analyzing experimental data of water confined by membranes and could help us to understand the role of water in biological systems.

Since the first isolation of graphene, the importance of van der Waals (vdW) interactions has become increasingly recognized in the burgeoning field of layered materials. In this work, infrared nanoimaging techniques and theoretical modeling are used to unravel the critical role played by interfacial vdW interactions in governing the stability of violet phosphorus (VP)—a recently rediscovered wide bandgap p‐type semiconductor—when exfoliated on different substrates. It is demonstrated that vdW interactions with the underlying substrate can have a profound influence on the stability of exfoliated VP flakes and investigate how these interactions are affected by flake thickness, substrate properties (e.g., substrate hydrophilicity, surface roughness), and the exfoliation process. These findings highlight the key role played by interfacial vdW interactions in governing the stability and physical properties of layered materials, and can be used to guide substrate selection in the preparation and study of this important class of materials. Infrared nanoimaging techniques and theoretical modeling unravel the critical role played by interfacial van der Waals (vdW) interactions in governing the stability of violet phosphorus (VP) when exfoliated on different substrates. It is investigated that how these interactions are affected by flake thickness, substrate hydrophilicity, and surface roughness.

Proton transfer via hydronium and hydroxide ions in water is ubiquitous. It underlies acid–base chemistry, certain enzyme reactions, and even infection by the flu. Despite two centuries of investigation, the mechanism underlying why hydroxide diffuses slower than hydronium in water is still not well understood. Herein, we employ state-of-the-art density-functional-theory-based molecular dynamics—with corrections for non-local van der Waals interactions, and self-interaction in the electronic ground state—to model water and hydrated water ions. At this level of theory, we show that structural diffusion of hydronium preserves the previously recognized concerted behaviour. However, by contrast, proton transfer via hydroxide is less temporally correlated, due to a stabilized hypercoordination solvation structure that discourages proton transfer. Specifically, the latter exhibits non-planar geometry, which agrees with neutron-scattering results. Asymmetry in the temporal correlation of proton transfer leads to hydroxide diffusing slower than hydronium.

High-throughput DFT calculations are key to screening existing/novel materials, sampling potential energy surfaces, and generating quantum mechanical data for machine learning. By including a fraction of exact exchange (EXX), hybrid functionals reduce the self-interaction error in semi-local DFT and furnish a more accurate description of the underlying electronic structure, albeit at a high computational cost that often prohibits such high-throughput applications. To address this challenge, we have constructed SeA (SeA=SCDM+exx+ACE), a robust, accurate, and efficient framework for high-throughput condensed-phase hybrid DFT in the PWSCF module of Quantum ESPRESSO (QE) by combining: (1) the non-iterative selected columns of the density matrix (SCDM) orbital localization scheme, (2) a black-box and linear-scaling EXX algorithm (exx), and (3) adaptively compressed exchange (ACE). Across a diverse set of non-equilibrium (H\\(_2\\)O)\\(_64\\) configurations (with densities spanning 0.4 g/cm $^3$ $-\\(1.7 g/cm\\)^3\\(), SeA yields a one\\)-\\(two order-of-magnitude speedup (~8X\\)-\\(26X) in the overall time-to-solution compared to PWSCF(ACE) in QE (~78X\\)-\\(247X speedup compared to the conventional EXX implementation) and yields energies, ionic forces, and other properties with high fidelity. As a proof-of-principle high-throughput application, we trained a deep neural network (DNN) potential for ambient liquid water at the hybrid DFT level using SeA via an actively learned data set with ~8,700 (H\\)_2\\(O)\\)_{64}\\( configurations. Using an out-of-sample set of (H\\)_2\\(O)\\)_{512}$configurations (at non-ambient conditions), we confirmed the accuracy of this SeA-trained potential and showcased the capabilities of SeA by computing the ground-truth ionic forces in this challenging system containing > 1,500 atoms.

We explore how anharmonicity, nuclear quantum effects (NQE), many-body dispersion interactions, and Pauli repulsion influence thermal properties of dispersion-bound molecular crystals. Accounting for anharmonicity with \\(ab\\) \\(initio\\) molecular dynamics yields cell parameters accurate to within 2% of experiment for a set of pyridine-like molecular crystals at finite temperatures and pressures. From the experimental thermal expansion curve, we find that pyridine-I has a Debye temperature just above its melting point, indicating sizable NQE across the entire crystalline range of stability. We find that NQE lead to a substantial volume increase in pyridine-I (\\( 40\\)% more than classical thermal expansion at \\(153\\) K) and attribute this to intermolecular Pauli repulsion promoted by intramolecular quantum fluctuations. When predicting delicate properties such as the thermal expansivity, we show that many-body dispersion interactions and sophisticated treatments of Pauli repulsion are needed in dispersion-bound molecular crystals.

We carry out X-ray absorption spectroscopy experiment at oxygen K-edge in croconic acid (C5H2O5) crystal as a prototype of ferroelectric organic molecular solid, whose electric polarization is generated by proton transfer. The experimental spectrum is well reproduced by the electron-hole excitation theory simulations from configuration generated by ab initio molecular dynamics simulation. When inversion symmetry is broken in ferroelectric state, the hydrogen bonding environment on the two bonded molecules become inequivalent. Such a difference is sensitively probed by the bound excitation in the pre-edge, which are strongly localized on the excited molecules. Our analysis shows that a satellite peak in the pre-edge will emerge at higher excitation energy which serves as a clear signature of ferroelectricity in the material.

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 H\\(_2\\)O and D\\(_2\\)O. 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.

We perform ab initio molecular dynamics (AIMD) simulation of liquid water in the canonical ensemble at ambient conditions using the SCAN meta-GGA functional approximation, and carry out systematic comparisons with the results obtained from the GGA-level PBE functional, and Tkatchenko-Scheffler van der Waals (vdW) dispersion correction inclusive PBE functional. We analyze various properties of liquid water including radial distribution functions, oxygen-oxygen-oxygen triplet angular distribution, tetrahedrality, hydrogen bonds, diffusion coefficients, ring statistics, density of states, band gaps, and dipole moments. We find that the SCAN functional is generally more accurate than the other two functionals for liquid water by not only capturing the intermediate-range vdW interactions but also mitigating the overly strong hydrogen bonds prescribed in PBE simulations. We also compare the results of SCAN-based AIMD simulations in the canonical and isothermal-isobaric ensembles. Our results suggest that SCAN provides a reliable description for most structural, electronic, and dynamical properties in liquid water.

We have synthesized boron nitride nanotubes (BNNTs) in an arc in presence of boron and nitrogen species only, without transition metals. We find that BNNTs are often attached to pure boron nanoparticles, suggesting that root-growth is a likely mechanism for their formation. To gain further insight into this process we have studied key mechanisms for root growth of BNNTs on the surface of a liquid boron droplet by ab initio molecular dynamics simulations. We find that nitrogen atoms reside predominantly on the droplet surface where they organize to form boron nitride islands below 2400 K. To minimize contact with the liquid particle underneath, the islands assume non-planar configurations that are likely precursors for the thermal nucleation of cap structures. Once formed, the caps are stable and can easily incorporate nitrogen and boron atoms at their base, resulting in further growth. Our simulations support the root-growth mechanism of BNNTs and provide comprehensive evidence of the active role played by liquid boron.