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At least 1,171 records · Page 65
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Composite Qdrift-product formulas for quantum and classical simulations in real and imaginary time

Recent study has shown that it can be advantageous to implement a composite channel that partitions the Hamiltonian H for a given simulation problem into subsets A and B such that H = A + B , where the terms in A are simulated with a Trotter-Suzuki channel and the B terms are randomly sampled via the Qdrift algorithm. Here we extend Qdrift and composite product formulas to imaginary time, formulating candidate classical algorithms for quantum Monte Carlo calculations. We upper bound the induced Schatten- 1 → 1 norm on both imaginary-time Qdrift and composite channels. Another recent result demonstrated that simulations of lattice Hamiltonians containing geometrically local interactions can be improved using a Lieb-Robinson argument to decompose H into subsets that contain only terms supported on that subset of the lattice. Here, we provide a quantum algorithm by unifying this result with the composite approach into “local composite channels” and we upper bound the diamond distance. We provide exact numerical simulations of algorithmic cost by counting the number of gates of the form e − i H j t and e − H j β to meet a certain error tolerance ε . In doing so, we optimize the partitioning into sets A and B using gradient boosted tree models from machine learning. These numerical studies are important given that product formulas have been historically known to outperform analytic upper bounds. We show constant factor advantages for a variety of interesting Hamiltonians, the maximum of which is a ≈ 20 -fold speedup that occurs in the simulation of Jellium. Published by the American Physical Society 2024

Pocrnic, Matthew (ORCID:0000000203089376)↗
DOE OSTI2023

On the Feasibility of Simulation-Driven Portfolio Scheduling for Cyberinfrastructure Runtime Systems

Runtime systems that automate the execution of applications on distributed cyberinfrastructures need to make scheduling decisions. Researchers have proposed many scheduling algorithms, but most of them are designed based on analytical models and assumptions that may not hold in practice. The literature is thus rife with algorithms that have been evaluated only within the scope of their underlying assumptions but whose practical effectiveness is unclear. It is thus difficult for developers to decide which algorithm to implement in their runtime systems.To obviate the above difficulty, we propose an approach by which the runtime system executes, throughout application execution, simulations of this very execution. Each simulation is for a different algorithm in a scheduling algorithm portfolio, and the best algorithm is selected based on simulation results. The main objective of this work is to evaluate the feasibility and potential merit of this portfolio scheduling approach, even in the presence of simulation inaccuracy, when compared to the traditional one-algorithm approach. We perform this evaluation via a case study in the context of scientific workflows. Our main finding is that portfolio scheduling can outperform the best one-algorithm approach even in the presence of relatively large simulation inaccuracies.

Casanova, Henri↗
DOE OSTI2024

Mapping structural and dynamic divergence across the MBOAT family

Membrane-bound O-acyltransferases (MBOATs) are membrane-embedded enzymes that catalyze acyl chain transfer to a diverse group of substrates, including lipids, small molecules, and proteins. MBOATs share a conserved structural core, despite wide-ranging functional specificity across both prokaryotes and eukaryotes. The structural basis of catalytic specificity, regulation and interactions with the surrounding environment remain uncertain. Here, we combine comparative molecular dynamics (MD) simulations with bioinformatics to assess molecular and interactional divergence across the family. In simulations, MBOATs differentially distort the bilayer depending on their substrate type. Additionally, we identify lipid binding sites surrounding reactant gates in the surrounding membrane. Complementary bioinformatic analyses reveal a conserved role for re-entrant loop-2 in MBOAT fold stabilization and a key hydrogen bond bridging DGAT1 dimerization. Finally, we predict differences in MBOAT solvation and water gating properties. These data are pertinent to the design of MBOAT-specific inhibitors that encompass dynamic information within cellular mimetic environments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Frameworks, Algorithms, and Scalable Technologies for Mathematics (FASTMath) SciDAC Institute

As computational models scale to larger computers, the rate at which they produce data has far outstripped the same computers ability to write that data and further the file systems ability to store that data. Almost all of the SciDAC applications, but especially those related to fusion solve very large scale PDEs whose scientific output his impacted by this problem. To gain access to dynamics in an exascale simulation that are not identifiable a priori and to make that dynamical data available to machine learning requires fundamental research in the area of in situ data data analytics. Here data analytics includes compression, visualization, uncertainty quantification, and machine learning. This in situ data analytics will enable on-the-fly spatial and temporal compression of solution dynamics, expose that space-time compressed field to machine learning algorithms that have been specialized to work with dynamically evolving data (existing machine learning algorithms treat data sets as static), greatly improving the opportunity for machine learning to provide feedback to the compression, all within an ongoing simulation, without the need to write data to files. The same concepts are also being applied to uncertainty quantification and multi-fidelity modeling which have similar needs for spatial and temporal compression of the ongoing exascale simulation to perform either without the typical, unacceptable writing of data to files.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Machine learning prediction of glass transition temperature of conjugated polymers from chemical structure

Predicting the glass transition temperature (T g ) is of critical importance as it governs the thermomechanical performance of conjugated polymers (CPs). Here, we report a predictive modeling framework to predict T g of CPs through the integration of machine learning (ML), molecular dynamics (MD) simulations, and experiments. With 154 T g data collected, an ML model is developed by taking simplified “geometry” of six chemical building blocks as molecular features, where side-chain fraction, isolated rings, fused rings, and bridged rings features are identified as the dominant ones for T g . MD simulations further unravel the fundamental roles of those chemical building blocks in dynamical heterogeneity and local mobility of CPs at a molecular level. The developed ML model is demonstrated for its capability of predicting T g of several new high-performance solar cell materials to a good approximation. The established predictive framework facilitates the design and prediction of T g of complex CPs, paving the way for addressing device stability issues that have hampered the field from developing stable organic electronics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

SpecSims: A Scalable Speculative Tree-based Simulation Cloning Framework for Finite Memory Machines

Simulation cloning is a technique in which cloned simulations whose state spaces differ partially from their parent simulation due to intervening events are spawned at runtime and concurrently advanced. It is a powerful method to carry out what-if analysis by speculatively exploring and evaluating the impact of various permutations of intervening cascade of events. Due to the exponential growth in the number of possible clones even for a small number of distinct intervening events, the practical efficacy of the approach is often severely limited by the maximum available memory of the computing host. In this paper, we introduce a novel speculative simulation cloning framework that executes a simulation cloning campaign capable of efficiently exploring an exponentially large space of clone simulations created by permutation of intervening events under a finite memory constraint. We provide a theoretical analysis of the runtime characteristics of our proposed approach and highlight its novel advantages such as memory-aware and as-long-as-needed execution. Furthermore, in support of our analytical findings and to demonstrate its practical feasibility, we implement a prototype of the cloning framework on a shared memory system and report its performance characteristics in the context of a heat diffusion simulation, and a power grid simulation subject to cascading disruptions from geomagnetic disturbances.

Simulation framework↗
DOE OSTI2023

Non-linear Wire Rope Isolator Model to Enhance Transportation Simulation of Fragile Equipment

Abstract Introduction Wire rope isolators are often used as means of vibration isolation for the transportation of fragile machine equipment. When deployed as suspension systems in a transportation scenario, a simple spring–damper model is often used to predict the dynamic loads that act on the structure being transported. However, this simple model has proved to be too simplistic to be used in the development of an accurate numerical model that can predict the vibration levels experienced by the inner components of fragile equipment during its transportation. This paper describes the experimental tests conducted on a wire rope isolator used for the transportation of the prototype SSR1 cryomodule. Materials and methods A hysteretic Bouc–Wen model has been used to analytically describe the force–deformation relationship of the wire rope isolators. The developed model of the isolator has been implemented in a larger model to simulate the actual transportation of the prototype SSR1 cryomodule, a section of the new PIP-II linear accelerator under construction at Fermilab. A series of multibody dynamic simulations with rigid and flexible components was used to numerically determine the acceleration of some critical components. Results and conclusions An actual experimental transportation was simulated using two numerical models: the developed Bouc–Wen model and a conventional spring–damper model. It is shown how the Bouc–Wen formulation of the isolator characteristics drastically improves the correspondence between experimental and simulated results if compared to a spring–damper model, especially in the range of 0–30 Hz which is the most critical for transportation problems.

42 ENGINEERING↗
DOE OSTI2021

Ab Initio Direct Dynamics

The reactivity and dynamics of molecular systems can be explored computationally by classical trajectory calculations. The traditional approach involves fitting a functional form of a potential energy surface (PES) to the energies from a large number of electronic structure calculations and then integrating numerous trajectories on this fitted PES to model the molecular dynamics. The ever-decreasing cost of computing and continuing advances in computational chemistry software have made it possible to use electronic structure calculations directly in molecular dynamics simulations without first having to construct a fitted PES. In this “on-the-fly” approach, every time the energy and its derivatives are needed for the integration of the equations of motion, they are obtained directly from quantum chemical calculations. This approach started to become practical in the mid-1990s as a result of increased availability of inexpensive computer resources and improved computational chemistry software. The application of direct dynamics calculations has grown rapidly over the last 25 years and would require a lengthy review article. The present Account is limited to some of our contributions to methods development and various applications. To improve the efficiency of direct dynamics calculations, we developed a Hessian-based predictor-corrector algorithm for integrating classical trajectories. Hessian updating made this even more efficient. Furthermore, this approach was also used to improve algorithms for following the steepest descent reaction paths. For larger molecular systems, we developed an extended Lagrangian approach in which the electronic structure is propagated along with the molecular structure. Strong field chemistry is a rapidly growing area, and to improve the accuracy of molecular dynamics in intense laser fields, we included the time-varying electric field in a novel predictor-corrector trajectory integration algorithm. Since intense laser fields can excite and ionize molecules, we extended our studies to include electron dynamics. Specifically, we developed code for time-dependent configuration interaction electron dynamics to simulate strong field ionization by intense laser pulses. Our initial application of ab initio direct dynamics in 1994 was to CH 2 O → H 2 + CO; the calculated vibrational distributions in the products were in very good agreement with experiment. In the intervening years, we have used direct dynamics to explore energy partitioning in various dissociation reactions, unimolecular dissociations yielding three fragments, reactions with branching after the transition state, nonstatistical dynamics of chemically activated molecules, dynamics of molecular fragmentation by intense infrared laser pulses, selective activation of specific dissociation channels by aligned intense infrared laser fields, angular dependence of strong field ionization, and simulation of sequential double ionization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

N -representability violations in truncated equation-of-motion coupled-cluster methods

One-electron reduced density matrices (1RDMs) from equation-of-motion (EOM) coupled-cluster with single and double excitations (CCSD) calculations are analyzed to assess their N-representability (i.e., whether they are derivable from a physical N-electron state). We identify EOM-CCSD stationary states whose 1RDMs violate either ensemble-state N-representability conditions or pure-state conditions known as generalized Pauli constraints. As such, these 1RDMs do not correspond to any physical N-electron state. Unphysical states are also encountered in the course of time-dependent EOM-CC simulations; when an external field drives transitions between a pair of stationary states with pure-state N-representable 1RDMs, the 1RDM of the time-dependent state can violate ensemble-state conditions. Furthermore, these observations point to potential challenges in interpreting the results of time-dependent EOM-CCSD simulations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Adaptive Conformer Sampling for Property Prediction Using the Conductor-like Screening Model for Real Solvents

The valorization of lignocellulose-derived bioproducts requires effective separation from excessive water. Liquid–liquid extraction is a promising low-energy separation technology, but effective extraction requires solvent selection based on the thermodynamic properties of the bioproduct and solvent components. We propose a computational framework for predicting such properties by developing an adaptive conformer selection approach for use with COSMO-RS (conductor-like screening model for real solvents) calculations. In this framework, molecular dynamics simulations are used to generate many molecular structures (conformers) at representative temperatures in varying solvent environments. Conformers are then clustered based on structural metrics in a low-dimensional space and selected using a mixed-integer quadratic programming problem to iteratively insert a sampled conformer. At each iteration, we determine bioproduct properties using COSMO-RS. Here, we demonstrate the capability of the proposed framework on representative bioproducts to show convergence of the adaptive sampling toward experimentally measured properties with fewer calculations than required by random conformer sampling, enabling the improved screening of solvent systems for liquid-phase separation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Molecular dynamics simulation of metallic Al–Ce liquids using a neural network machine learning interatomic potential

Al-rich Al-Ce alloys have the possibility of replacing heavier steel and cast-irons for use in high-temperature applications. Knowledge about the structures and properties of Al-Ce alloys at liquid state is vital for optimizing the manufacture process to produce desired allows. However, reliable molecular dynamics simulation of Al-Ce alloy systems remains a great challenge due to the lack of accurate Al-Ce interatomic potential. In this work, an artificial neural network (ANN) deep machine learning (ML) method is used to develop a reliable interatomic potential for Al-Ce alloy. Ab initio molecular dynamics (AIMD) simulation data on Al-Ce liquid with small unit cell (~200 atoms) and on the known Al-Ce crystalline compounds are collected to train the interatomic potential using ANN-ML. The obtained ANN-ML model reproduces well the energies, forces, and atomic structure of Al 90 Ce 10 liquid and crystalline phases of Al-Ce compounds in comparison with ab initio results. The developed ANN-ML potential is applied in molecular dynamics simulations to study the structures and properties of metallic Al 90 Ce 10 liquid, which would provide useful insight for guiding experimental process to produce desired Al-Ce allows.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Fractional Laplacian spectral approach to turbulence in a dusty plasma monolayer

This work presents an analytical investigation of anomalous diffusion and turbulence in a dusty plasma monolayer, where energy transport across scales leads to the spontaneous formation of spatially disordered patterns. Many-body simulations of 10,000-particle dusty plasma monolayers are used to demonstrate how the global dynamics depend on the statistical properties of the dust assembly for realistic laboratory conditions. We find that disorder due to variations in the dust size distribution and charge-driven nonlocal interactions resulting in anomalous dust diffusion are key factors for the onset of instabilities. The resulting dynamics exhibit features of inertial turbulence over slightly more than half a decade of scales proportional or smaller than the Debye shielding length. These processes are examined analytically using a recently developed Fractional Laplacian Spectral (FLS) technique, which identifies the active energy channels as a function of scale, disorder concentration, and features of the nonlocal-interactions. The predictions from the theoretical (spectral) analysis demonstrate agreement with the results from the many-body (kinetic) simulations, thus providing a powerful tool for the study of active turbulence.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

The symmetric quasi-classical model using on-the-fly time-dependent density functional theory within the Tamm–Dancoff approximation

The primary computational challenge when simulating nonadiabatic ab initio molecular dynamics is the unfavourable compute costs of electronic structure calculations with molecular size. Simple electronic structure theories, like time-dependent density functional theory within the Tamm–Dancoff approximation (TDDFT/TDA), alleviate this cost for moderately sized molecular systems simulated on realistic time scales. Although TDDFT/TDA does have some limitations in accuracy, an appealing feature is that, in addition to including electron correlation through the use of a density functional, the cost of calculating analytic nuclear gradients and nonadiabatic coupling vectors is often computationally feasible even for moderately sized basis sets. Here in this work, some of the benefits and limitations of TDDFT/TDA are discussed and analysed with regard to its applicability as a ‘back-end’ electronic structure method for the symmetric quasi-classical Meyer–Miller model (SQC/MM). In order to investigate the benefits and limitations of TDDFT/TDA, SQC/MM is employed to predict and analyse a prototypical example of excited-state hydrogen transfer in gas-phase malonaldehyde. Then, the ring-opening dynamics of selenophene are simulated, which highlight some of the deficiencies of TDDFT/TDA. Additionally, some new algorithms are proposed that speed up the calculation of analytic nuclear gradients and nonadiabatic coupling vectors for a set of excited electronic states.

molecular dynamics↗
DOE OSTI2020

Imaging phonon dynamics with ultrafast electron microscopy: Kinematical and dynamical simulations

Ultrafast x-ray and electron scattering techniques have proven to be useful for probing the transient elastic lattice deformations associated with photoexcited coherent acoustic phonons. Indeed, femtosecond electron imaging using an ultrafast electron microscope (UEM) has been used to directly image the influence of nanoscale structural and morphological discontinuities on the emergence, propagation, dispersion, and decay behaviors in a variety of materials. Here, we describe our progress toward the development of methods ultimately aimed at quantifying acoustic-phonon properties from real-space UEM images via conventional image simulation methods extended to the associated strain-wave lattice deformation symmetries and extents. Using a model system consisting of pristine single-crystal Ge and a single, symmetric Lamb-type guided-wave mode, we calculate the transient strain profiles excited in a wedge specimen and then apply both kinematical- and dynamical-scattering methods to simulate the resulting UEM bright-field images. While measurable contrast strengths arising from the phonon wavetrains are found for optimally oriented specimens using both approaches, incorporation of dynamical scattering effects via a multi-slice method returns better qualitative agreement with experimental observations. Contrast strengths arising solely from phonon-induced local lattice deformations are increased by nearly an order of magnitude when incorporating multiple electron scattering effects. We also explicitly demonstrate the effects of changes in global specimen orientation on the observed contrast strength, and we discuss the implications for increasing the sophistication of the model with respect to quantification of phonon properties from UEM images.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Electrical Submersible Pumps: A System Modeling Approach for Power Quality Analysis With Variable Frequency Drives

This article proposes a high-level engineering guide to develop an integrated system model for power quality analysis in electrical submersible pump (ESP) applications with variable frequency drives (VFDs). Such analyses are troublesome in the industry due to the complexity of these systems. In this article, simple steps to perform system integration analyses of such arrangements, including torsional analysis, are developed. A simplified VFD-ESP model suitable for coupled electrical and mechanical analysis in steady state is proposed in all their configurations of practical interest. Such a model can be easily implemented in common simulation software, significantly reducing engineering efforts for implementation and analysis. The focus of the proposed model is the prediction of ESP failures that might result from a poor power quality caused by VFDs. Analytical expressions of different types of harmonics in these systems, as well as their accurate locations in the frequency domain, including their interharmonics and common-mode harmonics, are derived for this purpose. Further, the effectiveness of the proposed model is verified through offline and real-time hybrid simulation results. Finally, a comparison between simulation results obtained using the proposed model and measurements collected on a down-scale laboratory prototype is carried out to demonstrate the accuracy of the suggested modeling approach.

42 ENGINEERING↗
DOE OSTI2024

Active oscillatory associative memory

Traditionally, physical models of associative memory assume conditions of equilibrium. Here, we consider a prototypical oscillator model of associative memory and study how active noise sources that drive the system out of equilibrium, as well as nonlinearities in the interactions between the oscillators, affect the associative memory properties of the system. Our simulations show that pattern retrieval under active noise is more robust to the number of learned patterns and noise intensity than under passive noise. To understand this phenomenon, we analytically derive an effective energy correction due to the temporal correlations of active noise in the limit of short correlation decay time. We find that active noise deepens the energy wells corresponding to the patterns by strengthening the oscillator couplings, where the more nonlinear interactions are preferentially enhanced. Using replica theory, we demonstrate qualitative agreement between this effective picture and the retrieval simulations. Our work suggests that the nonlinearity in the oscillator couplings can improve memory under nonequilibrium conditions.

Chemistry↗