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At least 757 records · Page 42

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DOE OSTI2024

E(n)-Equivariant cartesian tensor message passing interatomic potential

Machine learning potential (MLP) has been a popular topic in recent years for its capability to replace expensive first-principles calculations in some large systems. Meanwhile, message passing networks have gained significant attention due to their remarkable accuracy, and a wave of message passing networks based on Cartesian coordinates has emerged. However, the information of the node in these models is usually limited to scalars, and vectors. In this work, we propose High-order Tensor message Passing interatomic Potential (HotPP), an E(n) equivariant message passing neural network that extends the node embedding and message to an arbitrary order tensor. By performing some basic equivariant operations, high order tensors can be coupled very simply and thus the model can make direct predictions of high-order tensors such as dipole moments and polarizabilities without any modifications. The tests in several datasets show that HotPP not only achieves high accuracy in predicting target properties, but also successfully performs tasks such as calculating phonon spectra, infrared spectra, and Raman spectra, demonstrating its potential as a tool for future research.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Quantum algorithms for geologic fracture networks

Abstract Solving large systems of equations is a challenge for modeling natural phenomena, such as simulating subsurface flow. To avoid systems that are intractable on current computers, it is often necessary to neglect information at small scales, an approach known as coarse-graining. For many practical applications, such as flow in porous, homogenous materials, coarse-graining offers a sufficiently-accurate approximation of the solution. Unfortunately, fractured systems cannot be accurately coarse-grained, as critical network topology exists at the smallest scales, including topology that can push the network across a percolation threshold. Therefore, new techniques are necessary to accurately model important fracture systems. Quantum algorithms for solving linear systems offer a theoretically-exponential improvement over their classical counterparts, and in this work we introduce two quantum algorithms for fractured flow. The first algorithm, designed for future quantum computers which operate without error, has enormous potential, but we demonstrate that current hardware is too noisy for adequate performance. The second algorithm, designed to be noise resilient, already performs well for problems of small to medium size (order 10–1000 nodes), which we demonstrate experimentally and explain theoretically. We expect further improvements by leveraging quantum error mitigation and preconditioning.

58 GEOSCIENCES↗

DOE OSTI2025

Origin of large variations of current on/off ratio and switching voltage in atomically thin memristors: an exascale ab initio transport study

Nonvolatile resistive switching in two-dimensional monolayers opens a new avenue for high-density memory/computing devices. However, questions remain as to why the current on/off ratio and switching voltage vary significantly among different devices. Here, we simulate electronic transport of large systems consisting of a h-BN monolayer sandwiched by gold electrodes, enabled by an implementation of the nonequilibrium Green’s function method in the exascale density functional theory (DFT) code: Real-space MultiGrid. Systematic calculations reveal that the wide range of on/off ratios is due to variations in interface distances between the electrode and h-BN that significantly modulate their wavefunction overlap. In addition, DFT calculations demonstrate that the energy barrier of a gold atom dissociating from the electrode to h-BN increases dramatically with the interface distance, thereby explaining the strong dependence of the switching voltage on distance. Our work demonstrates the significance of interface distance in governing the current on/off ratio and switching voltage.

Electronic devices↗

DOE OSTI2023

Emergence of competing electronic states from non-integer nuclear charges

Understanding many-electron phenomena with competing near-degenerate electronic states is of fundamental importance to chemistry and condensed matter physics. One of the most significant challenges for exploring such many-electron phenomena is the necessity for large system sizes in order to realize competing states, far beyond those practical for first-principles methods. Here, we show how allowing non-integer nuclear charges expands the space of computationally tractable electron systems that host competing electronic states. The emergence of competing electronic states from non-integer nuclear charges is exemplified in the simple 2-electron H 2 molecule and used to examine the microscopic structure of doped quasi-1D cuprate chains, showing how non-integer nuclear charges can open a window for first-principles calculations of difficult many-electron phenomena.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2022

Magnetic reconnection in the era of exascale computing and multiscale experiments

Astrophysical plasmas have the remarkable ability to preserve magnetic topology, which inevitably gives rise to the accumulation of magnetic energy within stressed regions including current sheets. This stored energy is often released explosively through the process of magnetic reconnection, which produces a reconfiguration of the magnetic field, along with high-speed flows, thermal heating and nonthermal particle acceleration. Either collisional or kinetic dissipation mechanisms are required to overcome the topological constraints, both of which have been predicted by theory and validated with in situ spacecraft observations or laboratory experiments. However, major challenges remain in understanding magnetic reconnection in large systems, such as the solar corona, where the collisionality is weak and the kinetic scales are vanishingly small in comparison with macroscopic scales. The plasmoid instability or formation of multiple plasmoids in long, reconnecting current sheets is one possible multiscale solution for bridging this vast range of scales, and new laboratory experiments are poised to study these regimes. In conjunction with these efforts, we anticipate that the coming era of exascale computing, together with the next generation of observational capabilities, will enable new progress on a range of challenging problems, including the energy build-up and onset of reconnection, partially ionized regimes, the influence of magnetic turbulence and particle acceleration.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

Supervised learning of a chemistry functional with damped dispersion

Abstract Kohn–Sham density functional theory is widely used in chemistry, but no functional can accurately predict the whole range of chemical properties, although recent progress by some doubly hybrid functionals comes close. Here, we optimized a singly hybrid functional called CF22D with higher across-the-board accuracy for chemistry than most of the existing non-doubly hybrid functionals by using a flexible functional form that combines a global hybrid meta-nonseparable gradient approximation that depends on density and occupied orbitals with a damped dispersion term that depends on geometry. We optimized this energy functional by using a large database and performance-triggered iterative supervised training. We combined several databases to create a very large, combined database whose use demonstrated the good performance of CF22D on barrier heights, isomerization energies, thermochemistry, noncovalent interactions, radical and nonradical chemistry, small and large systems, simple and complex systems and transition-metal chemistry.

Liu, Yiwei (ORCID:0000000288126163)↗

DOE OSTI2020

Structural evolution of two-dimensional silicates using a “bond-switching” algorithm

Silicates are the most abundant materials in the earth's crust. In recent years, two-dimensional (2D) versions of them grown on metal supports (known as bilayer silicates) have allowed their study in detail down to the atomic scale. These structures are self-containing. Furthermore, they are not covalently bound to the metal support but interact with it through van der Waals forces. Like their three-dimensional counterparts, the 2D-silicates can form both crystalline and vitreous structures. Furthermore, the interconversion between vitreous to crystalline structures has been experimentally observed at the nanoscale. While theoretical work has been carried out to try to understand these transformations, a limitation for ab initio methods, and even molecular dynamics methods, is the computational cost of studying large systems and long timescales. In this work, we present a simple and computationally inexpensive approach, that can be used to represent the evolution of bilayer silicates using a bond-switching algorithm. This approach allows reaching equilibrium ring size distributions as a function of a parameter that can be related to the ratio between temperature and the energy required for the bond-switching event. The ring size distributions are compared to experimental data available in the literature.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2022

Electrolyte clusters as hydrogen sponges: diffusion Monte Carlo simulations

Here, we carry out Diffusion Monte Carlo simulations of up to five hydrogen molecules aggregated with two Stockmayer clusters that solvate a single lithium ion. The first one contains six point dipole solvent particles with parameters tuned to emulate nitromethane. The second cluster is a relative large system investigated recently [G. DiEmma, S. Kalette, and E. Curotto, Chem. Phys. Lett. 2019, 725, 80–86]. In both cases we find that the aggregated hydrogen molecules perturb significantly the ground state of the host cluster and form a distorted tetrahedral cage around the Li + ion. The fifth hydrogen molecule is absorbed by the larger Stockmayer cluster while remaining in the proximity of the solvated charge.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

A first-principles machine-learning force field for heterogeneous ice nucleation on microcline feldspar

The formation of ice in the atmosphere affects precipitation and cloud properties, and plays a key role in the climate of our planet. Although ice can form directly from liquid water at deeply supercooled conditions, the presence of foreign particles can aid ice formation at much warmer temperatures. Over the past decade, experiments have highlighted the remarkable efficiency of feldspar minerals as ice nuclei compared to other particles present in the atmosphere. However, the exact mechanism of ice formation on feldspar surfaces has yet to be fully understood. Here, we develop a first-principles machine-learning model for the potential energy surface aimed at studying ice nucleation at microcline feldspar surfaces. The model is able to reproduce with high-fidelity the energies and forces derived from density-functional theory (DFT) based on the SCAN exchange and correlation functional. Our training set includes configurations of bulk supercooled water, hexagonal and cubic ice, microcline, and fully-hydroxylated feldspar surfaces exposed to vacuum, liquid water, and ice. In this work, we apply the machine-learning force field to study different fully-hydroxylated terminations of the (100), (010), and (001) surfaces of microcline exposed to vacuum. Our calculations suggest that terminations that do not minimize the number of broken bonds are preferred in vacuum. We also study the structure of supercooled liquid water in contact with microcline surfaces, and find that water density correlations extend up to around 10 Å from the surfaces. Finally, we show that the force field maintains a high accuracy during the simulation of ice formation at microcline surfaces, even for large systems of around 30,000 atoms. Future work will be directed towards the calculation of nucleation free energy barriers and rates using the force field developed herein, and understanding the role of different microcline surfaces on ice nucleation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Rapidly convergent quantum Monte Carlo using a Chebyshev projector

The multireference coupled-cluster Monte Carlo (MR-CCMC) algorithm is a determinant-based quantum Monte Carlo (QMC) algorithm that is conceptually similar to Full Configuration Interaction QMC (FCIQMC). It has been shown to offer a balanced treatment of both static and dynamic correlation while retaining polynomial scaling, although application to large systems with significant strong correlation remained impractical. In this paper, we document recent algorithmic advances that enable rapid convergence and a more black-box approach to the multireference problem. These include a logarithmically scaling metric-tree-based excitation acceptance algorithm to search for determinants connected to the reference space at the desired excitation level and a symmetry-screening procedure for the reference space. We show that, for moderately sized reference spaces, the new search algorithm brings about an approximately 8-fold acceleration of one MR-CCMC iteration, while the symmetry screening procedure reduces the number of active reference space determinants with essentially no loss of accuracy. We also introduce a stochastic implementation of an approximate wall projector, which is the infinite imaginary time limit of the exponential projector, using a truncated expansion of the wall function in Chebyshev polynomials. Notably, this wall-Chebyshev projector can be used to accelerate any projector-based QMC algorithm. We show that it requires significantly fewer applications of the Hamiltonian to achieve the same statistical convergence. We benchmark these acceleration methods on the beryllium and carbon dimers, using initiator FCIQMC and MR-CCMC with basis sets up to cc-pVQZ quality.

Zhao, Zijun↗

DOE OSTI2023

Fluctuations in heavy ion collisions and global conservation effects

Subensemble is a type of statistical ensemble which is the generalization of grand canonical and canonical ensembles. The subensemble acceptance method (SAM) provides general formulas to correct the cumulants of distributions in heavy-ion collisions for the global conservation of all QCD charges. The method is applicable for an arbitrary equation of state and sufficiently large systems, such as those created in central collisions of heavy ions. The new fluctuation measures insensitive to global conservation effects are presented. The main results are illustrated in the hadron resonance gas and van der Waals fluid frameworks.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

Spinodal enhancement of fluctuations in nucleus-nucleus collisions

Subensemble Acceptance Method (SAM) [1, 2] is an essential link between measured event-by-event fluctuations and their grand canonical theoretical predictions such as lattice QCD. The method allows quantifying the global conservation law effects in fluctuations. In its basic formulation, SAM requires a sufficiently large system such as created in central nucleus-nucleus collisions and sufficient space-momentum correlations. Directly in the spinodal region of the First Order Phase Transition (FOPT) different approximations should be used that account for finite size effects. Thus, we present the generalization of SAM applicable in both the pure phases, metastable and unstable regions of the phase diagram [3]. Obtained analytic formulas indicate the enhancement of fluctuations due to crossing the spinodal region of FOPT and are tested using molecular dynamics simulations. A rather good agreement is observed. Using transport model calculations with interaction potential we show that the spinodal enhancement of fluctuations survives till the later stages of collision via the memory effect [4]. However, at low collision energies the space-momentum correlation is not strong enough for this signal to be transferred to second and third order cumulants measured in momentum subspace. This result agrees well with recent HADES data on proton number fluctuations at $\sqrt{S_{NN}}$ = 2.4 GeV which are found to be consistent with the binomial momentum space acceptance [5].

Poberezhnyuk, Roman↗

DOE OSTI2020

A parametric SPICE model for the simulation of spark gap switches

The use of SPICE-based software for the simulation of pulsed power systems—even large complex systems—has become commonplace in the pulsed power community. This is in contrast to earlier work in the field that relied on specially developed simulation codes such as Sandia’s Screamer or the Navy Research Lab’s Bertha, which natively incorporated models for common pulsed power components such as spark gap switches. Unlike these programs, SPICE programs provide a simple and familiar user interface and wide availability. However, SPICE programs do not include realistic models for key pulsed power circuit devices—including the spark gap switch. While simple switch models do exist in SPICE programs, these can only crudely approximate the behavior of a spark gap. This effort focuses on developing an SPICE circuit model for a gas-filled spark gap switch that is physically realistic while being simple enough to permit simulations to run in reasonable times on typical personal computers. Detailed information is provided for implementation in two common versions of SPICE: LTspice and Orcad PSPICE. Adaptation to other SPICE programs is possible with minimal modification. The model is intended as a design tool that uses physical parameters as inputs to connect it directly to the development of useable pulsed power systems. Data collected from the operation of a high-pressure pulsed-charged switch and a complete 12-stage Marx generator have been used to demonstrate the implementation and accuracy of the model over a wide range of parameters.

Pouncey, J. Cameron (ORCID:0000000153500816)↗

DOE OSTI2020

DFTB+, a software package for efficient approximate density functional theory based atomistic simulations

DFTB+ is a versatile community developed open source software package offering fast and efficient methods for carrying out atomistic quantum mechanical simulations. By implementing various methods approximating density functional theory (DFT), such as the density functional based tight binding (DFTB) and the extended tight binding method, it enables simulations of large systems and long timescales with reasonable accuracy while being considerably faster for typical simulations than the respective ab initio methods. Based on the DFTB framework, it additionally offers approximated versions of various DFT extensions including hybrid functionals, time dependent formalism for treating excited systems, electron transport using non-equilibrium Green’s functions, and many more. DFTB+ can be used as a user-friendly standalone application in addition to being embedded into other software packages as a library or acting as a calculation-server accessed by socket communication. We give an overview of the recently developed capabilities of the DFTB+ code, demonstrating with a few use case examples, discuss the strengths and weaknesses of the various features, and also discuss on-going developments and possible future perspectives.

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2020

Liquid to crystal Si growth simulation using machine learning force field

Machine learning force field (ML-FF) has emerged as a potential promising approach to simulate various material phenomena for large systems with ab initio accuracy. However, most ML-FFs have been used to study the phenomena relatively close to the equilibrium ground states. In this work, we have studied a far from equilibrium system of liquid to crystal Si growth using ML-FF. Here, we found that our ML-FF based on ab initio decomposed atomic energy can reproduce all the aspects of ab initio simulated growth, from local energy fluctuations to transition temperatures, to diffusion constant, and growth rates. We have also compared the growth simulation with the Stillinger-Weber classical force field and found significant differences. A procedure is also provided to correct a systematic fitting bias in the ML-FF training process, which exists in all training models, otherwise critical results like transition temperature will be wrong.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Simulations of activities, solubilities, transport properties, and nucleation rates for aqueous electrolyte solutions

This article reviews recent molecular simulation studies of "collective" properties of aqueous electrolyte solutions, specifically free energies and activity coefficients, solubilities, nucleation rates of crystals, and transport coefficients. These are important fundamental properties for biology and geoscience, but also relevant for many technological applications. Their determination from molecular-scale calculations requires large systems and long sampling times, as well as specialized sampling algorithms. As a result, such properties have not typically been taken into account during optimization of force field parameters; thus, they provide stringent tests for the transferability and range of applicability of proposed molecular models. There has been significant progress on simulation algorithms to enable the determination of these properties with good statistical uncertainties. Comparisons of simulation results to experimental data reveal deficiencies shared by many commonly used models. Moreover, there appear to exist specific tradeoffs within existing modeling frameworks, so that good prediction of some properties is linked to poor prediction for specific other properties. For example, non-polarizable models that utilize full charges on the ions generally fail to predict accurately both activity coefficients and solubilities; the concentration dependence of viscosity and diffusivity for these models is also incorrect. Scaled-charge models improve the dynamic properties and could also perform well for solubilities, but fail in the prediction of nucleation rates. Even models that do well at room temperature for some properties generally fail to capture their experimentally observed temperature dependence. Finally, the main conclusion from the present review is that qualitatively new physics will need to be incorporated in future models of electrolyte solutions to allow description of collective properties for broad ranges of concentrations, temperatures, and solvent conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Fully variational incremental CASSCF

We report the complete-active-space self-consistent field (CASSCF) method is a canonical electronic structure theory that holds a central place in conceptualizing and practicing first principles simulations. For application to realistic molecules, however, the CASSCF must be approximated to circumvent its exponentially scaling computational costs. Applying the many-body expansion - also known as the method of increments - to CASSCF (iCASSCF) has been shown to produce a polynomially scaling method that retains much of the accuracy of the parent theory and is capable of treating full valence active spaces. Due to an approximation made in the orbital gradient, the orbital parameters of the original iCASSCF formulation could not be variationally optimized, which limited the accuracy of its nuclear gradient. Herein, a variational iCASSCF is introduced and implemented, where all parameters are fully optimized during energy minimization. This method is able to recover electronic correlations from the full valence space in large systems, produce accurate gradients, and optimize stable geometries as well as transition states. Demonstrations on challenging test cases, such as the oxoMn(salen)Cl complex with 84 electrons in 84 orbitals and the automerization of cyclobutadiene, show that the fully variational iCASSCF is a powerful tool for describing challenging molecular chemistries.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Simplified tuning of long-range corrected density functionals for use in symmetry-adapted perturbation theory

Long considered a failure, second-order symmetry-adapted perturbation theory (SAPT) based on Kohn–Sham orbitals, or SAPT0(KS), can be resurrected for semiquantitative purposes using long-range corrected density functionals whose asymptotic behavior is adjusted separately for each monomer. Here, as in other contexts, correct asymptotic behavior can be enforced via “optimal tuning” based on the ionization energy theorem of density functional theory, but the tuning procedure is tedious, expensive for large systems, and comes with a troubling dependence on system size. Here, we show that essentially identical results are obtained using a fast, convenient, and automated tuning procedure based on the size of the exchange hole. In conjunction with “extended” (X)SAPT methods that improve the description of dispersion, this procedure achieves benchmark-quality interaction energies, along with the usual SAPT energy decomposition, without the hassle of system-specific tuning.

74 ATOMIC AND MOLECULAR PHYSICS↗