Engineering Papers⌕ Search

Engineering topics

Perez, Danny

Publications and source records attributed to Perez, Danny.

At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Frontier: Exploring Exascale

As the US Department of Energy (DOE) computing facilities began deploying petascale systems in 2008, DOE was already setting its sights on exascale. In that year, DARPA published a report on the feasibility of reaching exascale. The report authors identified several key challenges in the pursuit of exascale including power, memory, concurrency, and resiliency. That report informed the DOE's computing strategy for reaching exascale. With the deployment of Oak Ridge National Laboratory's Frontier supercomputer, we have officially entered the exascale era. In this paper, we discuss Frontier's architecture, how it addresses those challenges, and describe some early application results from Oak Ridge Leadership Computing Facility's Center of Excellence and the Exascale Computing Project.

Atchley, Scott {Leadership Computing}↗
DOE OSTI2023

Combining machine-learned and empirical force fields with the parareal algorithm: application to the diffusion of atomistic defects

We numerically investigate an adaptive version of the parareal algorithm in the context of molecular dynamics. This adaptive variant has been originally introduced in [1]. We focus here on test cases of physical interest where the dynamics of the system is modelled by the Langevin equation and is simulated using the molecular dynamics software LAMMPS. In this work, the parareal algorithm uses a family of machine-learning spectral neighbor analysis potentials (SNAP) as fine, reference, potentials and embedded-atom method potentials (EAM) as coarse potentials. We consider a self-interstitial atom in a tungsten lattice and compute the average residence time of the system in metastable states. Our numerical results demonstrate significant computational gains using the adaptive parareal algorithm in comparison to a sequential integration of the Langevin dynamics. We also identify a large regime of numerical parameters for which statistical accuracy is reached without being a consequence of trajectorial accuracy.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Improving Estimation of the Koopman Operator with Kolmogorov–Smirnov Indicator Functions

It has become common to perform kinetic analysis using approximate Koopman operators that transform high-dimensional timeseries of observables into ranked dynamical modes. The key to the practical success of the approach is the identification of a set of observables that form a good basis on which to expand the slow relaxation modes. Good observables are, however, difficult to identify a priori and suboptimal choices can lead to significant underestimations of characteristic time scales. Leveraging the representation of slow dynamics in terms of Hidden Markov Models (HMM), we propose a simple and computationally efficient clustering procedure to infer surrogate observables that form a good basis for slow modes. Here, we apply the approach to an analytically solvable model system as well as on three protein systems of different complexities. We consistently demonstrate that the inferred indicator functions can significantly improve the estimation of the leading eigenvalues of Koopman operators and correctly identify key states and transition time scales of stochastic systems, even when good observables are not known a priori.

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

Arbitrarily accurate, nonparametric coarse graining with Markov renewal processes and the Mori–Zwanzig formulation

Stochastic dynamics, such as molecular dynamics, are important in many scientific applications. However, summarizing and analyzing the results of such simulations is often challenging due to the high dimension in which simulations are carried out and, consequently, due to the very large amount of data that are typically generated. Coarse graining is a popular technique for addressing this problem by providing compact and expressive representations. Coarse graining, however, potentially comes at the cost of accuracy, as dynamical information is, in general, lost when projecting the problem in a lower-dimensional space. This article shows how to eliminate coarse-graining error using two key ideas. First, we represent coarse-grained dynamics as a Markov renewal process. Second, we outline a data-driven, non-parametric Mori–Zwanzig approach for computing jump times of the renewal process. Numerical tests on a small protein illustrate the method.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Diffusion of small anti-Schottky clusters in UO 2

The aggregation of irradiation-induced defect clusters in UO 2 leads to the formation of dislocation loops and cavities which contribute swelling and ultimately produce deleterious effects on the fuel. Despite their fundamental role in fuel evolution, the kinetics of these defect clusters are not currently well understood. In this work, we investigate the diffusive behavior of interstitial clusters via Molecular Dynamics (MD) simulations. In this work, our investigation considers a range of defect cluster sizes, each composed of N UO 2 units or anti-Schottky defects. We report a complex cluster size - mobility relation; increasing cluster size corresponds to an increase in mobility up until a critical size (N=4) where diffusivity reaches a maximum, after which the trend reverts and further increasing size corresponds to a decrease in mobility. The rapid migration observed correlates well with the very low barriers reported in a few historic experimental studies. Further analysis of the cluster shape and orientation reveals a strong structural preference for near-planar configurations oriented normal to the $\langle$100$\rangle$ direction. Rotational energy barriers are found to be similar in both magnitude and trend to the observed diffusion barriers. Lastly, connections are drawn to the behavior of large defect clusters including the formation of (1/3) $\langle$111$\rangle$ Frank dislocation loops.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Architector

Architector is a 3D chemical structure generation software package designed to take minimal 2D information about ligands and metal centers and generates chemically sensible 3D conformers and stereochemistry of the organometallic compounds. Architector is capable of high-throughput in-silico construction of s-, p-, d-, and f-block organometallic complexes. Architector represents a transformative step towards cross-periodic table computational design of metal complex chemistry.

Yang, Ping↗
DOE OSTI2022

Ab initio investigation of elastic properties of dilute Cu alloys for high-gradient accelerating structures

The elastic properties and solid solution strengthening of several Cu alloys that may be promising for high gradient applications were studied using ab initio calculations based on density functional theory. Specifically, the temperature and solute concentration dependence of elastic properties including bulk and shear moduli, thermal expansion coefficient, and heat capacity of these alloys were calculated. We found that the bulk and shear moduli are linearly dependent on the solute concentration in the dilute regime and change significantly for different Cu alloys. The thermal expansion coefficient and heat capacity are comparatively less sensitive to solute concentrations. Solid solution strengthening is observed to be linearly correlated with the change of lattice parameter of Cu. This work provides an in-depth understanding of the elastic properties of Cu alloys, which is key for predicting their performance at high gradient in normal conducting accelerating structures.

36 MATERIALS SCIENCE↗