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We model, via large-scale molecular dynamics simulations, the isothermal compression of low-density amorphous ice (LDA) to generate high-density amorphous ice (HDA) and the corresponding decompression extending to negative pressures to recover the low-density amorphous phase (LDAHDA). Both LDA and HDA are nearly hyperuniform and are characterized by a dynamical HBN, showing that amorphous ices are nonstatic materials and implying that nearly hyperuniformity can be accommodated in dynamical networks. In correspondence with both the LDA-to-HDA and the HDA-to-LDAHDAphase transitions, the (partial) activation of rotational degrees of freedom activates a cascade effect that induces a drastic change in the connectivity and a pervasive reorganization of the HBN topology which, ultimately, break the samples’ hyperuniform character. Key to this effect is the rapid rate at which changes occur, and not their magnitude. The inspection of structural properties from the short- to the long-range shows that signatures of metastability are present at all length-scales, hence providing further solid evidence in support of the liquid–liquid critical point scenario. LDA and LDAHDAdiffer in terms of HBN and structural properties, implying that they are distinct low-density glasses. Our work unveils the role of molecular rotations in the phase transitions between amorphous ices and shows how the unfreezing of rotational degrees of freedom generates a cascade effect that propagates over multiple length-scales. Our findings greatly improve our basic understanding of water and amorphous ices and can potentially impact the field of molecular network-forming materials at large.

Machine learning models are rapidly becoming widely used to simulate complex physicochemical phenomena with ab initioaccuracy. Here, we use one such model as well as direct density functional theory (DFT) calculations to investigate the phase equilibrium of water, hexagonal ice (Ih), and cubic ice (Ic), with an eye toward studying ice nucleation. The machine learning model is based on deep neural networks and has been trained on DFT data obtained using the SCAN exchange and correlation functional. We use this model to drive enhanced sampling simulations aimed at calculating a number of complex properties that are out of reach of DFT-driven simulations and then employ an appropriate reweighting procedure to compute the corresponding properties for the SCAN functional. This approach allows us to calculate the melting temperature of both ice polymorphs, the driving force for nucleation, the heat of fusion, the densities at the melting temperature, the relative stability of ices Ih and Ic, and other properties. We find a correct qualitative prediction of all properties of interest. In some cases, quantitative agreement with experiment is better than for state-of-the-art semiempirical potentials for water. Our results also show that SCAN correctly predicts that ice Ih is more stable than ice Ic.

By including a fraction of exact exchange (EXX), hybrid functionals reduce the self-interaction error in semilocal density functional theory (DFT) and thereby furnish a more accurate and reliable description of the underlying electronic structure in systems throughout biology, chemistry, physics, and materials science. However, the high computational cost associated with the evaluation of all required EXX quantities has limited the applicability of hybrid DFT in the treatment of large molecules and complex condensed-phase materials. To overcome this limitation, we describe a linear-scaling approach that utilizes a local representation of the occupied orbitals (e.g., maximally localized Wannier functions (MLWFs)) to exploit the sparsity in the real-space evaluation of the quantum mechanical exchange interaction in finite-gap systems. In this work, we present a detailed description of the theoretical and algorithmic advances required to perform MLWF-based ab initiomolecular dynamics (AIMD) simulations of large-scale condensed-phase systems of interest at the hybrid DFT level. We focus our theoretical discussion on the integration of this approach into the framework of Car–Parrinello AIMD, and highlight the central role played by the MLWF-product potential (i.e., the solution of Poisson’s equation for each corresponding MLWF-product density) in the evaluation of the EXX energy and wave function forces. We then provide a comprehensive description of the exxalgorithm implemented in the open-source Quantum ESPRESSOprogram, which employs a hybrid MPI/OpenMPparallelization scheme to efficiently utilize the high-performance computing (HPC) resources available on current- and next-generation supercomputer architectures. This is followed by a critical assessment of the accuracy and parallel performance (e.g., strong and weak scaling) of this approach when AIMD simulations of liquid water are performed in the canonical (NVT) ensemble. With access to HPC resources, we demonstrate that exxenables hybrid DFT-based AIMD simulations of condensed-phase systems containing 500–1000 atoms (e.g., (H2O)256) with a wall time cost that is comparable to that of semilocal DFT. In doing so, exxtakes us one step closer to routinely performing AIMD simulations of complex and large-scale condensed-phase systems for sufficiently long time scales at the hybrid DFT level of theory.

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. Even though the Grotthuss mechanism was proposed two centuries ago, it is still unclear why proton transfer via the hydroxide ion is slower than that via hydronium. Advanced ab initio molecular dynamics simulations now show that it is because proton transfer via hydroxide is less temporally correlated than transfer via hydronium.

The MB-pol many-body potential accurately predicts many properties of water, including cluster, liquid phase, and vapor–liquid equilibrium properties, but its high computational cost can make applying it in large-scale simulations quite challenging. In order to address this limitation, we developed a “deep potential” neural network (DPMD) model based on the MB-pol potential for water. We find that a DPMD model trained on mostly liquid configurations yields a good description of the bulk liquid phase but severely underpredicts vapor–liquid coexistence densities. By contrast, adding cluster configurations to the neural network training set leads to a good agreement for the vapor coexistence densities. Liquid phase densities under supercooled conditions are also represented well, even though they were not included in the training set. These results confirm that neural network models can combine accuracy and transferability if sufficient attention is given to the construction of a representative training set for the target system.

We propose a quantum Monte Carlo approach to solve the many-body Schrödinger equation for the electronic ground state. The method combines optimization from variational Monte Carlo and propagation from auxiliary field quantum Monte Carlo in a way that significantly alleviates the sign problem. In application to molecular systems, we obtain highly accurate results for configurations dominated by either dynamic or static electronic correlation.

Measuring the thermal conductivity (κ) of water at extreme conditions is a challenging task, and few experimental data are available. We predict κ for temperatures and pressures relevant to the conditions of the Earth mantle, between 1,000 and 2,000 K and up to 22 GPa. We employ close to equilibrium molecular dynamics simulations and a deep neural network potential fitted to density functional theory data. We then interpret our results by computing the equation of state of water on a fine grid of points and using a simple model for κ. We find that the thermal conductivity is weakly dependent on temperature and monotonically increases with pressure with an approximate square-root behavior. In addition, we show how the increase of κ at high pressure, relative to ambient conditions, is related to the corresponding increase in the sound velocity. Although the relationships between the thermal conductivity, pressure and sound velocity established here are not rigorous, they are sufficiently accurate to allow for a robust estimate of the thermal conductivity of water in a broad range of temperatures and pressures, where experiments are still difficult to perform.

In this work, we construct distinct first-principles-based machine-learning models of CO2, reproducing the potential energy surface of the PBE-D3, BLYP-D3, SCAN, and SCAN-rvv10 approximations of density functional theory. We employ the Deep Potential methodology to develop the models and consequently achieve a significant computational efficiency over ab initiomolecular dynamics (AIMD) that allows for larger system sizes and time scales to be explored. Although our models are trained only with liquid-phase configurations, they are able to simulate a stable interfacial system and predict vapor–liquid equilibrium properties, in good agreement with results from the literature. Because of the computational efficiency of the models, we are also able to obtain transport properties, such as viscosity and diffusion coefficients. We find that the SCAN-based model presents a temperature shift in the position of the critical point, while the SCAN-rvv10-based model shows improvement but still exhibits a temperature shift that remains approximately constant for all properties investigated in this work. We find that the BLYP-D3-based model generally performs better for the liquid phase and vapor–liquid equilibrium properties, but the PBE-D3-based model is better suited for predicting transport properties.

As a promising layered electrode material, TiS2-based capacitive deionization (CDI) devices for water desalination have attracted significant attention. However, TiS2/H2O interfacial features, potentially important for device optimization, remain unidentified. Using Deep Potential Molecular Dynamics (DPMD), we characterized distinct aqueous interfaces introduced by four TiS2terminations expected to be present as water intercalates into TiS2, namely, Armchair, Zigzag, Zigzag-L, and Zigzag-R. First, we assessed important representative physical properties of the system to validate the deep potentials (DPs). DPMD simulations agree well with experiments and first-principles simulations, suggesting the DPs are accurate and reliable. Subsequent simulations of these TiS2/water interfaces revealed how TiS2surface termination influences the structure of interfacial water. This effect is most evident in the first and second water layers close to the TiS2surface, and more pronounced when spontaneous dissociative adsorption of water occurs. The extent of water dissociation on each surface was evaluated using enhanced sampling. Zigzag-L is the only interface where proton transfer from adsorbed water to TiS2surface S atoms is thermodynamically and kinetically favored. The coexistence of surface four-fold-coordinated Ti (Ti4c) and one-fold-coordinated S (S1c) is found to be essential to making proton transfer feasible on the Zigzag-L surface. Furthermore, remaining unprotonated S1catoms can act as good proton acceptors after water dissociation. Thus, TiS2with Zigzag-L termination may be a surface to avoid in CDI device construction, given that pH fluctuations adversely affect performance. This work provides new understanding of TiS2/H2O interfacial features that could aid future design and optimization of TiS2-based CDI devices for water desalination.

Infrared spectroscopy in combination with density functional theory calculations has been widely used to characterize the structure of graphene oxide and its reduced forms. Yet, the synergistic effects of different functional groups, lattice defects, and edges on the vibrational spectra are not well understood. Here, we report first-principles calculations of the infrared spectra of graphene oxide performed on realistic, thermally equilibrated, structural models that incorporate lattice vacancies and edges along with various oxygen-containing functional groups. Models including adsorbed water are examined as well. Our results show that lattice vacancies lead to important blue and red shifts in the OH stretching and bending bands, respectively, whereas the presence of adsorbed water leaves these shifts largely unaffected. We also find unique infrared features for edge carboxyls resulting from interactions with both nearby functional groups and the graphene lattice. Comparison of the computed vibrational properties to our experiments clarifies the origin of several observed features and provides evidence that defects and edges are essential for characterizing and interpreting the infrared spectrum of graphene oxide.

In catalysis by metalloenzymes and in electrocatalysis by clusters related in structure and composition to the active components of such enzymes transition‐metal atoms can play a central role in the catalyzed redox reactions. Changes to their oxidation states (OSs) are critical for understanding the reactions. The OS is a local property and we introduce a new, generally useful local method for determining OSs, their changes, and the associated bonding changes and electron flow. The method is based on computing optimally localized orbitals (OLOs). With this method, we analyze two cases, superoxide reductase (SOR) and a proposed hydrogen‐producing model electrocatalyst [FeS2]/[FeFe]P, a modification of the active site of the diiron hydrogenase enzymes. Both utilize an under‐coordinated Fe site where a one‐electron reduction (for SOR) or a two‐electron reduction (for [FeFe]P) of the substrate occurs. We obtain the oxidation states of the Fe atoms and of their critical ligands, the changes of the bonds to those ligands, and the electron flow during the catalytic cycle, thereby demonstrating that OLOs constitute a powerful interpretive tool for unraveling reaction mechanisms by first‐principles computations.

Bacterial di-iron hydrogenases produce hydrogen efficiently from water. Accordingly, we have studied by first-principles molecular-dynamics simulations (FPMD) electrocatalytic hydrogen production from acidified water by their common active site, the [FeFe]Hcluster, extracted from the enzyme and linked directly to the (100) surface of a pyrite electrode. We found that the cluster could not be attached stably to the surface via a thiol link analogous to that which attaches it to the rest of the enzyme, despite the similarity of the (100) pyrite surface to the Fe4S4cubane to which it is linked in the enzyme. We report here a systematic sequence of modifications of the structure and composition of the cluster devised to maintain the structural stability of the pyrite/cluster complex in water throughout its hydrogen production cycle, an example of the molecular design of a complex system by FPMD.

We probe the bending characteristics of functionalized graphene sheets with the tip of an atomic force microscope. Individual sheets are transformed from a flat into a folded configuration. Sheets can be reversibly folded and unfolded multiple times, and the folding always occurs at the same location. This observation suggests that the folding and bending behavior of the sheets is dominated by pre-existing kink (or even fault) lines consisting of defects and/or functional groups.

The structures, interaction, and magnetic properties of Fe atoms with a single-wall carbon nanotube are investigated using Car−Parrinello molecular dynamics. The stability, band gap, Fermi energy, and total magnetic moment of the Fe-adsorbed single-wall carbon nanotube systems are found to depend on the location of the Fe atoms relative to the carbon nanotube surface. The confinement and the curvature of nanotubes have a strong effect on the relative stability of various structures corresponding to different positions of the Fe atom inside and outside carbon nanotubes. The Fe atoms inside a carbon nanotube are found to couple ferromagnetically.

Density-Functional-Theory (DFT) provides a general framework to deal with the ground-state energy of the electrons in many-atom systems. Its history dates back to the work of Thomas [1], Fermi [2] and Dirac [3] who devised approximate expressions for the kinetic energy [1, 2] and the exchange energy [3] of many-electron systems in terms of simple functionals of the local electron density. These ideas were further elaborated in the Xα method of Slater [4], until finally, the foundations of the modern theory were laid down in the mid-sixties by Kohn and collaborators [5, 6]. Since then but particularly in the last two decades the number of applications of DFT to electronic structure problems has grown dramatically. Today DFT is the method of choice for first-principles electronic structure calculations in condensed phase and complex molecular environments. DFT based approaches are used in a variety of disciplines ranging from condensed matter physics, to chemistry, materials science, biochemistry and biophysics. There are several reason for this success: (i) DFT makes the many-body electronic problem tractable at a numerical cost of self-consistent-field single particle calculations; (ii) despite the severe approximations made to the exchange and correlation energy functional, DFT calculations are usually sufficiently accurate to predict materials structures or chemical reactions products; (iii) currently available computational power and modern numerical algorithms make DFT calculations feasible for realistic models of systems like e.g. an interface between two crystalline materials, a carbon nanotube, or the active site of an enzyme.

We used classical molecular mechanics (MM) simulations and quantum mechanical (QM) structural relaxations to examine the active site of myosin when bound to ATP. Two conformations of myosin have been determined by x-ray crystallography. In one, there is no direct interaction between switch 2 and the nucleotide (open state). In the other (closed state), the universally conserved switch 2 glycine forms a hydrogen bond with a γ-phosphate oxygen. MM simulations indicate that the two states are thermodynamically stable and allow us to investigate the extent to which the P-loop, switch 1, and switch 2 are involved in hydrolysis. We find that the open structure has a higher affinity for ATP than the closed structure, and that ATP is distorted toward a transition state by interactions with the protein. We also examine how the structure of the binding site changes with either MgATP or CaATP as the nucleotide in myosin in the open conformer. Our analyses suggest that higher CaATPase rates occur because the leaving phosphate (Pi) group is more weakly bound and dissociation occurs faster. Finally, we validate the use of a particular formulation of a QM methodology (Car-Parrinello) to further refine the structures of the active site.

In the attempt to identify the physical origin of a first sharp diffraction peak (FSDP) in the concentration–concentration partial structure factor, we perform first-principle molecular dynamics of liquid GeSe2. We compare the structural properties of two set of simulations, which differ by their treatment of the electronic structure. This is achieved by considering different energy cutoffs defining the plane-wave basis sets, the higher energy cutoff leading to a moderate enhancement of the ionicity in the system. Both schemes give a neutron scattering factor in excellent agreement with experiment. The higher ionicity leads to small but perceptible modifications in some structural features related to short and intermediate range order, slightly improving the agreement with the experimental partial structure factors. In particular, a FSDP becomes discernable in the concentration–concentration structure factor. Although the magnitude of this peak is small compared to the experiment, this observation suggests that a good description of this peak could be obtained with an energy functional inducing an even higher ionicity.

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The authors review briefly the existing theoretical treatments of the various effects that contribute to the reduction of the energy gap in heavily doped Si, namely electron-electron and electron-impurity interactions and the effect of disorder in the impurity distribution. They then turn to the longstanding question why energy-gap reductions extracted from three different types of experiments have persistently produced values with substantial discrepancies, making it impossible to compare with theoretical values. First, they demonstrate that a meaningful comparison between theory and experiment can indeed be made if theoretical calculations are carried out for actual quantities that experiments measure, e.g. luminescence spectra, as recently done by Selloni and Pantelides. Then, they demonstrate that, independent of any theoretical calculations, the optical absorption spectra are fully consistent with the luminescence spectra and that the discrepancies in the energy-gap reductions extracted from the two sets of spectra are caused entirely by the curve-fitting procedures used in analyzing optical-absorption data. Finally, they show explicitly that, as already believed by many authors, energy-gap reductions extracted from electrical measurements on transistors do not correspond to true gap reductions. They identify two corrections that must be added to the values extracted from the electrical data in order to arrive at the true gap reductions and show that the resulting values are in good overall agreement with luminescence and absorption data. They, therefore, demonstrate that the observed reduction in emitter injection efficiency in bipolar transistors is not strictly due to a gap reduction, as generally believed, but to three very different effects.

Ab initio calculations on surface diffusion of Cu adatoms on Cu(001) are presented. The hopping mechanism with a calculated energy barrier of 0.69 eV is found to be favorable over the exchange mechanism with 0.97 eV. We find from the geometry relaxations that adatoms are significantly attracted to the surface and push away nearest-neighbor atoms in the surface. Lateral relaxations of atoms in the surface are larger than vertical ones.

We present the GPU version of DeePMD-kit, which, upon training a deep neural network model using ab initio data, can drive extremely large-scale molecular dynamics (MD) simulation with ab initio accuracy. Our tests show that for a water system of 12 , 582 , 912 atoms, the GPU version can be 7 times faster than the CPU version under the same power consumption. The code can scale up to the entire Summit supercomputer. For a copper system of 113 , 246 , 208 atoms, the code can perform one nanosecond MD simulation per day, reaching a peak performance of 86 PFLOPS (43% of the peak). Such unprecedented ability to perform MD simulation with ab initio accuracy opens up the possibility of studying many important issues in materials and molecules, such as heterogeneous catalysis, electrochemical cells, irradiation damage, crack propagation, and biochemical reactions. Program Title: DeePMD-kit CPC Library link to program files: https://doi.org/10.17632/phyn4kgsfx.1 Developer's repository link: https://doi.org/10.5281/zenodo.3961106 Licensing provisions: LGPL Programming language: C++/Python/CUDA Journal reference of previous version: Comput. Phys. Commun. 228 (2018), 178–184. Does the new version supersede the previous version?: Yes. Reasons for the new version: Parallelize and optimize the DeePMD-kit for modern high performance computers. Summary of revisions: The optimized DeePMD-kit is capable of computing 100 million atoms molecular dynamics with ab initio accuracy, achieving 86 PFLOPS in double precision. Nature of problem: Modeling the many-body atomic interactions by deep neural network models. Running molecular dynamics simulations with the models. Solution method: The Deep Potential for Molecular Dynamics (DeePMD) method is implemented based on the deep learning framework TensorFlow. Standard and customized TensorFlow operators are optimized for GPU. Massively parallel molecular dynamics simulations with DeePMD models on high performance computers are supported in the new version. [ABSTRACT FROM AUTHOR]