Refined description of liquid and supercooled silicon from ab initio simulations
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Remsing, Richard C.↗
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Using Neural Network Force Fields to Ascertain the Quality of Ab Initio Simulations of Liquid Water
Accurately simulating the properties of bulk water, despite the apparent simplicity of the molecule, is still a challenge. In order to fully understand and reproduce its complex phase diagram, it is necessary to perform simulations at the ab initio level, including quantum mechanical effects both for electrons and nuclei. This comes at a high computational cost, given that the structural and dynamical properties tend to require long timescales and large simulation cells. In this work, we evaluate the errors that density functional theory (DFT)-based simulations routinely incur into due time- and size-scale limitations. These errors are evaluated using neural-network-trained force fields that are accurate at the level of DFT methods. We compare different exchange and correlation potentials for properties of bulk water that require large timescales. Here we show that structural properties are less dependent on the system size and that dynamical properties such as the diffusion coefficient have a strong dependence on the simulation size and timescale. Our results facilitate comparisons of DFT-based simulation results with experiments and offer a path to discriminate between model and convergence errors in these simulations.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Ab Initio Simulations of a Supernova-driven Galactic Dynamo in an Isolated Disk Galaxy
We study the magnetic field evolution of an isolated spiral galaxy, using isolated Milky Way–mass galaxy formation simulations and a novel prescription for magnetohydrodynamic (MHD) supernova feedback. Our main result is that a galactic dynamo can be seeded and driven by supernova explosions, resulting in magnetic fields whose strength and morphology are consistent with observations. In our model, supernovae supply thermal energy and a low-level magnetic field along with their ejecta. The thermal expansion drives turbulence, which serves a dual role by efficiently mixing the magnetic field into the interstellar medium and amplifying it by means of a turbulent dynamo. The computational prescription for MHD supernova feedback has been implemented within the publicly available ENZO code and is fully described in this paper. This improves upon ENZO 's existing modules for hydrodynamic feedback from stars and active galaxies. We find that the field attains microgauss levels over gigayear timescales throughout the disk. The field also develops a large-scale structure, which appears to be correlated with the disk’s spiral arm density structure. We find that seeding of the galactic dynamo by supernova ejecta predicts a persistent correlation between gas metallicity and magnetic field strength. We also generate all-sky maps of the Faraday rotation measure from the simulation-predicted magnetic field, and we present a direct comparison with observations.
79 ASTRONOMY AND ASTROPHYSICS↗
Large-Scale Materials Modeling at Quantum Accuracy: Ab Initio Simulations of Quasicrystals and Interacting Extended Defects in Metallic Alloys
Ab initio electronic-structure has remained dichotomous between achievable accuracy and length-scale. Quantum many-body (QMB) methods realize quantum accuracy but fail to scale. Density functional theory (DFT) scales favorably but remains far from quantum accuracy. We present a framework that breaks this dichotomy by use of three interconnected modules: (i) invDFT: a methodological advance in inverse DFT linking QMB methods to DFT; (ii) MLXC: a machine-learned density functional trained with invDFT data, commensurate with quantum accuracy; (iii) DFT-FE-MLXC: an adaptive higher-order spectral finite-element (FE) based DFT implementation that integrates MLXC with efficient solver strategies and HPC innovations in FE-specific dense linear algebra, mixed-precision algorithms, and asynchronous compute-communication. Furthermore, we demonstrate a paradigm shift in DFT that not only provides an accuracy commensurate with QMB methods in ground-state energies, but also attains an unprecedented performance of 659.7 PFLOPS (43.1% peak FP64 performance) on 619,124 electrons using 8,000 GPU nodes of Frontier supercomputer.
density functional theory↗
Ab Initio Simulation of Amorphous Graphite
An amorphous graphite material has been predicted from molecular dynamics simulation using ab initio methods. Carbon materials reveal a strong proclivity to convert into a sp 2 network and then layer at temperatures near 3000 K within a density range of ca. 2.2–2.8 g/cm 3 . Each layer of amorphous graphite is a monolayer of amorphous graphene including pentagons and heptagons in addition to hexagons, and the planes are separated by about 3.1 Å. The layering transition has been studied using various structural and dynamical analyses. The transition is unique as one of partial ordering (long range order of planes and galleries, but topological disorder in the planes). The planes are quite flat, even though monolayer amorphous graphene puckers near pentagonal sites. Interplane cohesion is due partly to non-Van der Waals interactions. The structural disorder has been studied closely, especially the consequences of disorder to electronic transport. Finally, it is expected that the transition elucidated here may be salient to other layered materials.
42 ENGINEERING↗
Exposing Key Vibrational Contributions to Properties of Organic Molecular Solids with High Signal, Low Frequency Neutron Spectroscopy and Ab Initio Simulations
Stability and response of supramolecular forms is important to many areas in materials science, and contributions from vibrations can be crucial. In this study, we have collected the first spectra of organic molecular crystals and polymorphic cocrystals using the next-generation, high-signal VISION spectrometer in the far-infrared (FIR) and mid-infrared (MIR) range. Unambiguously different spectral signatures were found for carbamazepine and two polymorphs of the carbamazepine-saccharin cocrystal, including numerous modes undetectable with optical methods. For FIR-range frequencies, in addition to correct calculation of peak positions, accurate line-shape features were reproducible by simulation using ab initio vibrational calculations. Finally, high confidence spectral assignments and thermochemical information for this region are thus expected, and a benchmark for vibrational ab initio calculations is provided, creating a significant addition to methods used in investigation and design of organic materials.
36 MATERIALS SCIENCE↗
Accelerating Ab Initio Simulation via Nested Monte Carlo and Machine Learned Reference Potentials
As a corollary of the rapid advances in computing, ab initio simulation is playing an increasingly important role in modeling materials at the atomic scale. Two strategies are possible, ab initio Monte Carlo (AIMC) and molecular dynamics (AIMD) simulation. The former benefits from exact sampling from the correct thermodynamic distribution, while the latter is typically more efficient with its collective all-atom coordinate updates. In this study, using a relatively simple test model comprised of helium and argon, we show that AIMC can be brought up to, and even above, the performance levels of AIMD via a hybrid nested sampling/machine learning (ML) strategy. Here, ML provides an accurate classical reference potential (up to three-body explicit interactions) that can pilot long collective Monte Carlo moves that are accepted or rejected in toto à la nested Monte Carlo (NMC); this is in contrast to the single move nature of a naive implementation. Our proposed method only requires a small up front expense from evaluating the ab initio energies and forces of (100) random configurations for training. Importantly, our method does not totally rely on the trained, assuredly imperfect, interaction. We show that high performance and exact sampling at the desired level of theory can be realized even when the trained interaction has appreciable differences from the ab initio potential. Remarkably, at the highest levels of performance realized via our approach, a pair of statistically uncorrelated atomic configurations can be generated with (1) ab initio calculations.
74 ATOMIC AND MOLECULAR PHYSICS↗
Oxidation Protection with Amorphous Surface Oxides: Thermodynamic Insights from Ab Initio Simulations on Aluminum
Native surface films play a key role in the oxidation and corrosion protection of functional and structural materials. In this work, we introduce a fully ab initio approach for understanding the thermodynamic driving force behind the initial phase selection among amorphous and crystalline structures for a surface film growing on a crystalline substrate. We apply the approach to elucidate the competition among corundum (α), spinel (γ), and amorphous (am.) Al 2 O 3 films growing on aluminum metal. We show that the amorphous Al 2 O 3 film becomes thermodynamically the most stable form below around ~1 nm, that is, the relative energetic stabilities of thin polymorphic Al 2 O films follow am. < γ < α. As the film thickness increases, the relative stability relation first changes to γ < α < am. and then to the bulk limit of α < γ < am. The nanoscale γ films distort substantially to form exclusively four- and fivefold-coordinated Al–O polyhedra, lose the close-packed O framework, and become “amorphous-like”, that is, exhibit both short-range order and energetic characteristics that are commensurate with the amorphous form. Our findings offer a quantitative, first-principles confirmation for the early hypotheses on the thermodynamic stability of amorphous surface films and provide insights for the critical role they play in oxidation protection. Handling the complexities associated with the initial film growth, including bulk, surface, interface, and strain energy effects in realistically complex ab initio simulations, we expect this approach to contribute to understanding of the mechanism behind effective passivation films for aluminum alloys and beyond.
36 MATERIALS SCIENCE↗
Using Density-Corrected DFT to Understand Density-Driven and Functional-Dependent Errors in Ab Initio Simulations of the Hydrated Electron
The hydrated electron, an excess electron in liquid water, plays a crucial role in a plethora of chemical processes, motivating extensive research efforts to characterize its structure, dynamics, and reactivity in solution. Recent theoretical approaches to understanding this intriguing object have involved ab initio simulations based on density functional theory (DFT). Although DFT allows for the study of hydrated electron reactivity and quantum mechanical behavior, it is well-known that anionic systems can suffer from significant density-driven errors (DDEs). Density-corrected DFT (DC-DFT) provides a framework to mitigate such errors; the method reduces DDEs by replacing the self-consistent (SC) density associated with a given density functional with the Hartree–Fock (HF) density. Since HF densities tend to be more localized than DFT SC densities, the DC-DFT scheme significantly improves errors in calculations where the SC density is spuriously delocalized. Here, we investigate how the use of density correction affects the calculated properties of the DFT-simulated (PBEh) hydrated electron, a particularly challenging diffuse anionic system to simulate. First, we analyze charge delocalization in a system consisting of a model octahedral hydrated electron water cluster (the so-called Kevan structure) along with a spatially separated sulfur atom. We show that the use of density correction indeed reduces DDEs in comparison to a standard DFT global hybrid functional. We then propagate molecular dynamics trajectories of the hydrated electron using DC-DFT, where we find that DC further localizes electron density in the cavity region, a signature of reduced charge delocalization. Unfortunately, the decreased radius of gyration of the spin density and corresponding tightening of the local solvation structure from density correction causes predicted observables to deviate further from experimental measurements than when density correction is not employed. Here, we argue that DC’s worse agreement with experiment results from the removal of a fortuitous cancellation of errors that is intrinsic to the PBEh functional. This indicates that the difficulties with DFT to simulate hydrated electrons are primarily due to the inherent approximations in DFT rather than to density-driven errors.
Density functional theory↗
Model of ramp compression of diamond from ab initio simulations
Ramp compression experiments characterize high-pressure states of matter at temperatures well below those present in shock compression. However, because temperature is typically not directly measured during ramp compression, it is uncertain how much heating occurs under these shock-free conditions. Here, we performed a series of ab initio simulations on carbon in order to match the density-stress measurements of Smith et al. [Smith et al., Nature (London) 511, 330 (2014)]. We considered isotropically as well as uniaxially compressed solid carbon in the diamond and BC8 phases, with and without defects, as well as liquid carbon. Our idealized model ascribes heating during ramp compression to an initially uniaxially compressed cell transforming isochorically into an isotropically (hydrostatic equivalent) compressed state having lower internal energy, hence higher temperature so as to conserve energy. Multiple such heating events can occur during a single ramp experiment, leading to higher temperatures than with isentropic compression. Comparison with experiments shows that heating alone does not explain the equation of state measurements on diamond, instead implying that a significant uniaxial stress component remains present at high compression. The temperature predictions of our ramp compression model remain to be verified with future laboratory measurements.
58 GEOSCIENCES↗
HPC4Mfg with Samsung: Making semiconductor devices cool through HPC ab initio simulations
For decades, the semiconductor technology has followed the Moore’s law, making newer devices more powerful and energy efficient. Recently, however, it has reached a point where the performance and the energy efficiency of the device do not improve with the shrinking device size. One of the fundamental reasons of this deviation from the past trend is the interconnect resistance, which becomes larger with the shrinking size. The devices size is so small that the quantum mechanical effects can no longer be ignored and the traditional continuum simulation tools such as TCAD become inadequate. In this project, Samsung Semiconductor Inc. and Lawrence Berkeley National Laboratory has collaborated to perform first of kind device-scale ab initio simulations to optimize materials and interconnect morphology to minimize interconnect resistance. We have tested the use of LS3DF method and the PEtot_trans approach on top of the folded spectrum method (FSM) Escan code to calculate the scattering state, and to study various effects influence the interconnect conductivity. We found that, the LS3DF can be used to calculate such metallic system. On the other hand, the use of Escan code to solve the linear equation is not practical due to the slow convergence. We have implemented a Chebyshev filter technique to calculate a few hundred eigen states near the scattering state energy E, then use these eigen states as preconditioner to solve the linear equation. We have used this approach to study the different factors which affect the interconnect conductivity, including the shape, the point defect, the temperature, and the grain boundary.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Condensed Phase Water Molecular Multipole Moments from Deep Neural Network Models Trained on Ab Initio Simulation Data
Ionic solvation phenomena in liquids involve intense interactions in the inner solvation shell. For interactions beyond the first shell, the ion–solvent interaction energies result from the sum of many smaller-magnitude contributions that can still include polarization effects. Deep neural network (DNN) methods have recently found wide application in developing efficient molecular models that maintain near-quantum accuracy. In this work, we extend the DeePMD-kit code to produce accurate molecular multipole moments in the bulk and near interfaces. The new method is validated by comparing the DNN moments with those generated by ab initio simulations. The moments are used to compute the electrostatic potential at the center of a molecular-sized hydrophobic cavity in water. The results show that the fields produced by the DNN models are in quantitative agreement with the AIMD-derived values. These efficient methods will open the door to more accurate solvation models for large solutes such as proteins.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Morse-Smale Analysis of Ion Diffusion in Ab Initio Battery Materials Simulations
Ab initio molecular dynamics (AIMD) simulations are increasingly useful in modeling, optimizing and synthesizing materials in energy sciences. In solving Schrödinger’s equation, they generate the electronic structure of the simulated atoms as a scalar field. However, methods for analyzing these volume data are not yet common in molecular visualization. The Morse-Smale complex is a proven, versatile tool for topological analysis of scalar fields. In this paper, we apply the discrete Morse-Smale complex to analysis of first-principles battery materials simulations. We consider a carbon nanosphere structure used in battery materials research, and employ Morse-Smale decomposition to determine the possible lithium ion diffusion paths within that structure. Our approach is novel in that it uses the wavefunction itself as opposed distance fields, and that we analyze the 1-skeleton of the Morse-Smale complex to reconstruct our diffusion paths. Furthermore, it is the first application where specific motifs in the graph structure of the complete 1-skeleton define features, namely carbon rings with specific valence. We compare our analysis of DFT data with that of a distance field approximation, and discuss implications on larger classical molecular dynamics simulations.
25 ENERGY STORAGE↗
Li-Ion Diffusion Correlations in LiAlGeO 4 : Quasielastic Neutron Scattering and Ab Initio Simulation
Here, we investigated the impact of Li stoichiometry and host flexibility on Li + diffusion processes in LiAlGeO 4 at the microscopic level using quasielastic neutron scattering (QENS) and ab initio molecular dynamics (AIMD) simulations. Using sufficiently long AIMD trajectories, we could simulate the observed QENS signal and identify the localized dynamics of Li in crystalline LiAlGeO 4 . Such information is vital to identify the bottleneck of diffusion processes and design materials for battery application. Our AIMD simulations in LiAlGeO 4 reveal that the Li + conductivity can be significantly improved by manipulating the Li stoichiometry and/or host flexibility via amorphization. We determined that excess Li stoichiometry enhances the Coulomb repulsion of neighboring Li sites and softens the host structure to enable faster Li + diffusion along the hexagonal c-axis. In the amorphous structure, random orientations of AlO 4 and GeO 4 polyhedral units create a wide distribution of intersite distances and significantly soften the host structure, greatly enhancing the Li + diffusion. The simulations are used to understand the nature of diffusion, especially the role of the host structure, the possible hopping pathways, and the diffusion behavior in various structures of LiAlGeO 4 .
localized dynamics↗
Melts of CrCoNi-based high-entropy alloys: Atomic diffusion and electronic/atomic structure from ab initio simulation
High-entropy alloys (HEAs) are an emerging class of advanced structural alloys under extensive research; yet, the properties of the liquid states of these materials, which are relevant to their processing, have been far less explored. In this work, we utilize ab initio molecular dynamics simulations to investigate the melt properties of a representative HEA—the Cantor alloy—and its derivatives: CrMnFeCoNi, CrFeCoNi, and CrCoNi. The atomic dynamics of these melts at various temperatures are investigated, specifically to analyze their electronic and atomic structures, including charge transfer, pair distribution functions, and short-range order. Results are compared with existing information for the liquids of metallic glasses, which also typically contain multiple principal elements, but retain the amorphous state under moderate to fast cooling rates. The present results provide insights into the structural and bonding factors favoring solidification to single-phase solid solutions in HEAs.
36 MATERIALS SCIENCE↗
Liquid-Vapor Coexistence and Critical Point of Mg 2 SiO 4 From Ab Initio Simulations
Hypervelocity impact-driven vaporization is characteristic of late-stage planet formation. Yet the behavior and properties of liquid-vapor mixtures of planetary materials of interest are typically unknown. Multiphase equations of state used in hydrodynamic simulations of planet impacts therefore lack reliable data for this important phenomenon. Here, we present the first constraints on the liquid-vapor critical point and coexistence phase boundary of Mg2SiO4 computed from ab initio molecular dynamics simulations. We found that the vapor is depleted in magnesium and enriched in silica and oxygen, while the coexisting liquid is enriched in magnesium and depleted in oxygen, from which we infer vaporization is incongruent. The critical point was estimated from an equation of state fit to the data. The results are in line with recent calculations of MgSiO3 and together confirm that extant multiphase equation of state (EOS) models used in planetary accretion modeling significantly underestimate the amount of supercritical material postimpact.
58 GEOSCIENCES↗
Ab-Initio Simulation Studies of Cr Solvation in Fluoride Molten Salts
Understanding molten salt chemistry is essential in ongoing research of the molten salt nuclear reactor (MSR). In this context, detailed understanding of the mechanisms underlying selective oxidation of metal species, such as Cr, is required to guide the design of effective corrosion mitigation strategies in molten salts. An important starting point for such mechanistic understanding is knowledge of the solvation structure and its role in controlling metal speciation. In this work, we use ab initio molecular dynamics simulations to study the short-range (on the scale of the nearest-neighbor bond lengths) and medium-range (over length scales of several neighbor spacings) structure in three different fluoride melts with and without Cr addition; namely, 2KF-NaF, 2LiF-BeF 2 , and 3LiF-AlF3. We find that Cr 0 ,Cr 2+ ,Cr 3+ can each be coordinated by different numbers of F-, with the variance in coordination number decreasing as oxidation state increases, and that these coordination geometries are largely independent of solvent. The manner by which Cr changes the medium-range structure, however, is found to be solvent-dependent. While 2KF-NaF melts show short and medium range order that is highly dynamic, 2LiF-BeF 2 and 3LiF-AlF 3 are characterized by molecular associates that are relatively long-lived that organize into oligomer structures on larger length scales. Rather than being solvated by F- ions alone, we find that Cr can incorporate into and be solvated within this oligomer structure. Fluoroacidity, alone, may therefore prove too simple a metric for assessing the corrosivity of molten fluorides. As our work suggests, the ability of Cr to solvate must be understood in the context of the short- and medium-range structure of the solvent.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Root-growth of boron nitride nanotubes: experiments and ab initio simulations
We have synthesized boron nitride nanotubes (BNNTs) in an arc in presence of boron and nitrogen species. We find that BNNTs are often attached to large nanoparticles, suggesting that root-growth is a likely mechanism for their formation. Moreover, the tube-end nanoparticles are composed of boron, without transition metals, indicating that transition metals are not necessary for the arc synthesis of BNNTs. 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.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗