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Reeve, Samuel Temple

Publications and source records attributed to Reeve, Samuel Temple.

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Co-design for Particle Applications at Exascale

Co-design across the Exascale Computing Project (ECP) has been critical for both enabling science applications and bringing disparate communities together. Developing and porting applications to the various high-performance computing (HPC) architectures on pre-exascale and exascale computers has been quite challenging due to the diversity of hardware features and software stacks. The Co-design Center for Particle Applications (CoPA) has developed and enhanced the Cabana and PROGRESS/BML libraries to facilitate the creation of new particle applications, make existing particle applications exascale capable, and allow teams to explore new capabilities. Particle methods from atomistic, mesoscale, continuum, through cosmological scales have been built with Cabana, along with new possibilities for application coupling. Similarly, the PROGRESS/BML library has enabled quantum particle applications with linear algebra solvers to use advanced hardware. Across these CoPA-developed libraries, the co-design abstraction layer combines performance portability with math library support to facilitate separation of concerns and directly support science runs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

PDMATLAB2D: A Peridynamics MATLAB Two-dimensional Code

PDMATLAB2D is a meshfree peridynamics implementation in MATLAB suitable for simulation of two-dimensional fracture problems. The purpose of this code is twofold. First, it provides an entry-level peridynamics computational tool for educational and training purposes. Second, it serves as an accessible and easily modifiable computational tool for peridynamics researchers who would like to adapt the code for a multitude of peridynamics simulation scenarios. The current version of the code implements a bond-based brittle elastic peridynamic model and a critical stretch criterion for bond breaking. However, the code is designed to be extendable for other peridynamic models and computational features. In this paper, we provide an overview of the code structure and functions with illustrative examples. In conclusion, due to the integrated computation and postprocessing MATLAB capabilities, PDMATLAB2D can serve as an effective testbed for testing new constitutive models and advanced numerical features for peridynamics computations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

A Sparse Distributed Gigascale Resolution Material Point Method

In this paper, we present a four-layer distributed simulation system and its adaptation to the Material Point Method (MPM). The system is built upon a performance portable C++ programming model targeting major High-Performance-Computing (HPC) platforms. A key ingredient of our system is a hierarchical block-tile-cell sparse grid data structure that is distributable to an arbitrary number of Message Passing Interface (MPI) ranks. We additionally propose strategies for efficient dynamic load balance optimization to maximize the efficiency of MPI tasks. Our simulation pipeline can easily switch among backend programming models, including OpenMP and CUDA, and can be effortlessly dispatched onto supercomputers and the cloud. Finally, we construct benchmark experiments and ablation studies on supercomputers and consumer workstations in a local network to evaluate the scalability and load balancing criteria. We demonstrate massively parallel, highly scalable, and gigascale resolution MPM simulations of up to 1.01 billion particles for less than 323.25 seconds per frame with 8 OpenSSH-connected workstations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

ExaCA: A performance portable exascale cellular automata application for alloy solidification modeling

Modeling the as-solidified grain structures that form during alloy processing is a critical component in understanding process-property relationships, particularly for additive manufacturing (AM) where grain structure is very sensitive to processing conditions. While cellular automata (CA)-based models have proven able to predict aspects of microstructure for several alloys and AM process conditions, long run times and large resource sets required limit the utility and the problem size to which existing CA models can be applied. As part of the ExaAM project, an initiative within the Exascale Computing Project (ECP) to develop, test, and optimize an exascale-capable coupled and self-consistent model of AM parts, we developed ExaCA (https://github.com/LLNL/ExaCA) for the liquid–solid phase transformation in the wake of AM melt pools. The CA-based code is parallelized using MPI and the Kokkos programming model, the latter enabling simulation on both CPUs and GPUs within a single-source implementation. Here, we detail the steps taken to transform a baseline, MPI-based CA code into one that is performant on CPUs and GPUs. Performance testing of ExaCA on Summit (a pre-exascale machine at Oak Ridge National Laboratory) was used to quantify CPU–GPU speedup comparing with equal numbers of nodes. Testing showed comparable CPU performance to the MPI-only CA code and a 5-20x speedup when running AM-based test problems using GPUs. The improved performance of CA through GPU utilization and the performance portable nature of ExaCA will enable accurate part-scale modeling by harnessing the power of current and future generations of high performance computing resources. Future work will include improving the strong scaling of ExaCA on GPUs by reducing load imbalance associated with the locality of the problem, and continuing performance optimization across exascale hardware.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Cabana: A Performance Portable Library for Particle-Based Simulations

Particle-based simulations are ubiquitous throughout many fields of computational science and engineering, spanning the atomistic level with molecular dynamics (MD), to mesoscale particle-in-cell (PIC) simulations for solid mechanics, device-scale modeling with PIC methods for plasma physics, and massive N-body cosmology simulations of galaxy structures, with many other methods in between (Hockney & Eastwood, 1989). While these methods use particles to represent significantly different entities with completely different physical models, many low-level details are shared including performant algorithms for short- and/or long-range particle interactions, multi-node particle communication patterns, and other data management tasks such as particle sorting and neighbor list construction. Cabana is a performance portable library for particle-based simulations, developed as part of the Co-Design Center for Particle Applications (CoPA) within the Exascale Computing Project (ECP) (Alexander et al., 2020). The CoPA project and its full development scope, including ECP partner applications, algorithm development, and similar software libraries for quantum MD, is described in (Mniszewski et al., 2021). Cabana uses the Kokkos library for on-node parallelism (Edwards et al., 2014; Trott et al., 2022), enabling simulation on multi-core CPU and GPU architectures, and MPI for GPU-aware, multi-node communication. Cabana provides particle simulation capabilities on almost all current Kokkos backends, including serial execution, OpenMP (including OpenMP-Target for GPUs), CUDA (NVIDIA GPUs), HIP (AMD GPUs), and SYCL (Intel GPUs), providing a clear path for the coming generation of accelerator-based exascale hardware. Cabana builds on Kokkos by providing new particle data structures and particle algorithms resulting in a similar execution policy-based, node-level programming model that is intended to be used in addition to the core Kokkos library within an application. Cabana is designed as an application and physics agnostic, but particle-specific toolkit which can either be used to generate a new application, or to be used as needed in existing applications at various levels of invasiveness including through interfaces that wrap user memory in existing data structures.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

ExaAM: Metal additive manufacturing simulation at the fidelity of the microstructure

Additive manufacturing (AM), or 3D printing, of metals is transforming the fabrication of components, in part by dramatically expanding the design space, allowing optimization of shape and topology. However, although the physical processes involved in AM are similar to those of welding, a field with decades of experimental, modeling, simulation, and characterization experience, qualification of AM parts remains a challenge. The availability of exascale computational systems, particularly when combined with data-driven approaches such as machine learning, enables topology and shape optimization as well as accelerated qualification by providing process-aware, locally accurate microstructure and mechanical property models. We describe the physics components comprising the Exascale Additive Manufacturing simulation environment and report progress using highly resolved melt pool simulations to inform part-scale finite element thermomechanics simulations, drive microstructure evolution, and determine constitutive mechanical property relationships based on those microstructures using polycrystal plasticity. We report on implementation of these components for exascale computing architectures, as well as the multi-stage simulation workflow that provides a unique high-fidelity model of process–structure–property relationships for AM parts. In addition, we discuss verification and validation through collaboration with efforts such as AM-Bench, a set of benchmark test problems under development by a team led by the National Institute of Standards and Technology.

3D printing↗
DOE OSTI2021

Implementing a neural network interatomic model with performance portability for emerging exascale architectures

The two main thrusts of computational science are increasingly accurate predictions and faster calculations; to this end, the zeitgeist in molecular dynamics (MD) simulations is pursuing machine learned and data driven interatomic models, e.g. neural network potentials, and novel hardware architectures, e.g. GPUs. Current implementations of neural network potentials are orders of magnitude slower than traditional interatomic models and while looming exascale computing offers the ability to run large, accurate simulations with these models, achieving portable performance for MD with new and varied exascale hardware requires rethinking traditional algorithms, using novel data structures, and library solutions. We re-implement a neural network interatomic model in CabanaMD, an MD proxy application, built on libraries developed for performance portability. Our implementation shows significantly improved thread scaling in this complex kernel as compared to a current LAMMPS implementation, across both strong and weak scaling. Our single-source solution enables simulations up to 20 million atoms on a single CPU node and 4 million atoms with improved performance on a single GPU. Furthermore, we also explore parallelism and data layout choices (using flexible data structures called AoSoAs) and their effect on performance, seeing up to ~50% and ~5% improvements in performance on a GPU by choosing the right level of parallelism and data layout respectively.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Enabling particle applications for exascale computing platforms

The Exascale Computing Project (ECP) is invested in co-design to assure that key applications are ready for exascale computing. Within ECP, the Co-design Center for Particle Applications (CoPA) is addressing challenges faced by particle-based applications across four “sub-motifs”: short-range particle–particle interactions (e.g., those which often dominate molecular dynamics (MD) and smoothed particle hydrodynamics (SPH) methods), long-range particle–particle interactions (e.g., electrostatic MD and gravitational N-body), particle-in-cell (PIC) methods, and linear-scaling electronic structure and quantum molecular dynamics (QMD) algorithms. Our crosscutting co-designed technologies fall into two categories: proxy applications (or “apps”) and libraries. Proxy apps are vehicles used to evaluate the viability of incorporating various types of algorithms, data structures, and architecture-specific optimizations and the associated trade-offs; examples include ExaMiniMD, CabanaMD, CabanaPIC, and ExaSP2. Libraries are modular instantiations that multiple applications can utilize or be built upon; CoPA has developed the Cabana particle library, PROGRESS/BML libraries for QMD, and the SWFFT and fftMPI parallel FFT libraries. Success is measured by identifiable “lessons learned” that are translated either directly into parent production application codes or into libraries, with demonstrated performance and/or productivity improvement. The libraries and their use in CoPA’s ECP application partner codes are also addressed.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Multiferroic ground states in free standing perovskite-based nanodots: a density functional theory study

In this work, we use density functional theory to investigate the possibility of polar and multiferroic states in free-standing, perovskite-based nanodots at the atomic limit of miniaturization: single unit cells with terminations which allow centro-symmetry. We consider both A-O and B-O 2 terminations for three families of nanodots: (i) A = Ba with B = Ti, Zr, and Hf; (ii) A = Ca and Sr with B = Ti; and (iii) A = Na and K with B = Nb. We find all A–O terminated dots to be non-polar and to exhibit cubic symmetry (except for K 8 NbO 6 ), regardless of the presence of ferroelectricity in the bulk. In contrast, all the B–O 2 terminated nanodots considered relax to a non-cubic ground state. Rather surprisingly, all of these structures exhibit polar ground states (except NaNb 8 O 12 ). We propose a new structural parameter, the cluster tolerance factor (CTF), to determine whether a particular chemistry will result in a polar ground state nanodot, analogous to the Goldschmidt factor for bulk ferroelectrics. In addition, we find that all A–O terminated (except Ca 8 TiO 6 ) and all polar B–O 2 terminated nanodots are magnetic, where none show magnetism in the bulk. As with bulk systems, multiferroicity in the B–O 2 terminated dots originates from separation between spin density in peripheral B atoms and polarity primarily caused by the off-center central A atom. Our findings stress that surface termination plays a crucial role in determining whether ferroelectricity is completely suppressed in perovskite-based materials at their limit of miniaturization.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Complexion dictated thermal resistance with interface density in reactive metal multilayers

Multilayers composed of aluminum (A1) and platinum (Pt) exhibit a non-monotonic trend in thermal resistance with bilayer thickness as measured by time domain thermoreflectance. The thermal resistance initially increases with reduced bilayer thickness only to reach a maximum and then decrease with further shrinking of the multilayer period. These observations are attributed to the evolving impact of an intermixed amorphous complexion approximately 10 nm in thickness, which forms at each boundary between Al- and Pt-rich layers. Scanning transmission electron microscopy combined with energy dispersive X-ray spectroscopy find that the elemental composition of the complexion varies based on bilayer periodicity as does the fraction of the multilayer composed of this interlayer. These variations in complexion mitigate boundary scattering within the multilayers as shown by electronic transport calculations employing density functional theory and non-equilibrium Green's functions on amorphous structures obtained via finite temperature molecular dynamics. The lessening of boundary scattering reduces the total resistance to thermal transport leading to the observed non-monotonic trend thereby highlighting the central role of complexion on thermal transport within reactive metal multilayers.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Tuning martensitic transformations via coherent second phases in nanolaminates using free energy landscape engineering

In this work we explore the possibilities and limitations of using a coherent second phase to engineer the thermo-mechanical properties of a martensitic alloy by modifying the underlying free energy landscape that controls the transformation. We use molecular dynamics simulations of a model atomistic system where the properties of a coherent, nanoscale second phase can be varied systematically. With a base martensitic material that undergoes a temperature-induced transformation from a cubic austenite to a monoclinic martensite, simulations show significant ability to engineer the transformation temperatures, from a ~50% reduction to a ~200% increase, with 50 at.% of the cubic second phase. We establish correlations between the properties of the second phase, the transformation characteristics, and the microstructure via the free energy landscape of the twophase systems. Coherency stresses have a strong influence on the martensitic variants observed and can even cause the non-martensitic second phase to undergo a transformation. Reducing the stiffness of the second phase increases the transformation strain and modifies the martensitic microstructure, increasing the volume fraction of the transformed material. This increase in transformation strain is accompanied by a significant increase in Af and thermal hysteresis, while Ms remains unaltered. Our findings on the tunability of martensitic transformations can be used for informed searches of second phases to achieve desired material properties, such as achieving room temperature, lightweight shape memory alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Uncharacteristic second order martensitic transformation in metals via epitaxial stress fields

While most phase transformations, e.g., ferroelectric or ferromagnetic, can be first or second order depending on external applied fields, martensitic transformations in metallic alloys are nearly universally first order. We demonstrate that epitaxial stress originating from the incorporation of a tailored second phase can modify the free energy landscape that governs the phase transition and change its order from first to second. High-fidelity molecular dynamics simulations show a remarkable change in the character of the martensitic transformation in Ni–Al alloys near the critical point. We observe the continuous evolution of the transformation order parameter and scaling with power-law exponents comparable to those in other ferroic transitions exhibiting critical behavior. Our theoretical work provides a foundation for recent experimental and computational results on martensites near critical points.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Orientation Relationships of Pure Tin on Single Crystal Germanium Substrates

The limited number of independent β-Sn grain orientations resulting from the difficulty in nucleating β-Sn during solidification of Sn-based solders has a large effect on the resulting β-Sn grain size and, hence, on overall solder joint performance and reliability. This study analyzes the efficacy of Ge as a heterogeneous nucleation agent for β-Sn by observing the morphologies and orientation relationships of as-deposited, solid-state annealed, and liquid-state annealed pure Sn films on single crystal Ge (100), (110), and (111) substrates. Here, the results from scanning electron microscopy and electron backscatter diffraction showed that the as-deposited Sn films all deposited with a Sn (001)|| z-axis texture, regardless of the underlying Ge substrate orientation. Solid-state annealing at 150 °C for 5 min did not result in significant dewetting of the Sn films, and the films maintained their as-deposited texture of Sn (001)|| z-axis, regardless of the underlying Ge substrate orientation. Liquid-state annealing at 235 °C for 1 min resulted is large-scale dewetting of the Sn films and re-orientation of the Sn films on the various Ge substrates. After solidification, the Ge (100) and (110) single crystal substrates produced patches of dewetted grains of the same orientation but there were no consistent Sn grain textures after liquid-state annealing, suggesting no single orientation relationship. In contrast, solidification on Ge (111) single crystal substrates resulted in isolated grains with a single Sn film texture and an orientation relationship of (100) Sn ∥(111) Ge and [100] Sn ∥[110] Ge . Density Functional Theory simulations of the experimentally observed Ge (111) sample orientation relationship and the Ge/Sn cube-on-cube orientation relationship suggest favorable relative interfacial binding energies for both interface orientations.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Online simulation powered learning modules for materials science

Simulation tools are playing a significant role in materials science and engineering and beyond their well established importance in research and development, these tools have a vital pedagogical potential. We describe a set of online simulation tools and learning modules designed to help students explore important concepts in materials science where hands-on activities with high-fidelity simulations can provide insight not easily acquired otherwise. The online tools, which involve density functional theory and molecular dynamics simulations, have been designed with non-expert end-users in mind and only a few clicks are required to perform most simulations, yet they are powered by research-grade codes and expert users can access advanced options. All tools and modules are available for online simulation in nanoHUB.org and access is open and free of charge. Importantly, instructors and students do not need to download or install any software. The learning modules cover a range of topics from electronic structure of crystals and doping, plastic deformation in metals, and physical properties of polymers. These modules have been utilized in several core undergraduate courses at Purdue’s School of Materials Engineering, they are self contained, and are easy to incorporate into existing classes.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Functional uncertainty quantification for isobaric molecular dynamics simulations and defect formation energies

Functional uncertainty quantification (FunUQ) was recently proposed to quantify uncertainties in models and simulations that originate from input functions, as opposed to parameters. This paper extends here FunUQ to quantify uncertainties originating from interatomic potentials in isothermal-isobaric molecular dynamics (MD) simulations and to the calculation of defect formation energies. We derive and verify a computationally inexpensive expression to compute functional derivatives in MD based on perturbation theory. We show that this functional derivative of the quantities of interest (average internal energy, volume, and defect energies in our case) with respect to the interatomic potential can be used to predict those quantities for a different interatomic potential, without re-running the simulation. The codes and scripts to perform FunUQ in MD are freely available for download. In addition, to facilitate reproducibility and to enable use of best practices for the approach, we created Jupyter notebooks to perform FunUQ analysis on MD simulations and made them available for online simulation in nanoHUB. The tool uses cloud computing resources and users can view, edit, and run end-to-end workflows from a standard web-browser without the need to download or install any software.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Role of electronic thermal transport in amorphous metal recrystallization: A molecular dynamics study

Recrystallization of glasses is important in a wide range of applications including electronics and reactive materials. Molecular dynamics (MD) has been used to provide an atomic picture of this process, but prior work has neglected the thermal transport role of electrons, the dominant thermal carrier in metallic systems. We characterize the role of electronic thermal conductivity on the velocity of recrystallization in Ni using MD coupled to a continuum description of electronic thermal transport via a two-temperature model. Our simulations show that for strong enough coupling between electrons and ions, the increased thermal conductivity removes the heat from the exothermic recrystallization process more efficiently, leading to a lower effective temperature at the recrystallization front and, consequently, lower propagation velocity. We characterize how electron-phonon coupling strength and system size affect front propagation velocity. Interestingly, we find that initial recrystallization velocity increases with decreasing system size due to higher overall temperatures. Overall, we show that a more accurate description of thermal transport due to the incorporation of electrons results in better agreement with experiments.

McClure, Zachary D. (ORCID:0000000183300217)↗
DOE OSTI2017

Harnessing mechanical instabilities at the nanoscale to achieve ultra-low stiffness metals

Alloy and microstructure optimization have led to impressive improvements in the strength of engineering metals, while the range of Young’s moduli achievable has remained essentially unchanged. This is because stiffness is insensitive to microstructure and bounded by individual components in composites. Here we design ultra-low stiffness in fully dense, nanostructured metals via the stabilization of a mechanically unstable, negative stiffness state of a martensitic alloy by its coherent integration with a compatible, stable second component. Explicit large-scale molecular dynamics simulations of the metamaterials with state of the art potentials confirm the expected ultra-low stiffness while maintaining full strength. We find moduli as low as 2 GPa, a value typical of soft materials and over one order of magnitude lower than either constituent, defying long-standing composite bounds. Furthermore, such properties are attractive for flexible electronics and implantable devices. Our concept is generally applicable and could significantly enhance materials science design space.

36 MATERIALS SCIENCE↗