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Kent, Paul

Publications and source records attributed to Kent, Paul.

16 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Insights into the Properties of MXenes and MXene Analogs from Atomistic Simulation

We review the properties that have been predicted for MXenes and MXene analogs from computational simulation with a focus on structural and electronic properties, energy storage, and ion transport. Methods considered range from quantum mechanical approaches to classical molecular dynamics. We conclude by reviewing current limitations and outstanding questions for simulation and MXene properties that have been little explored to-date.

Muraleedharan, Murali Gopal↗
DOE OSTI2024

Software engineering to sustain a high-performance computing scientific application: QMCPACK

We provide an overview of the software engineering efforts and their impact in QMCPACK, a production-level ab-initio Quantum MonteCarlo open-source code targeting high-performance computing (HPC) systems. Aspects included are: (i) strategic expansion ofcontinuous integration (CI) targeting CPU, using GitHub Actions runners, and graphics processing units (GPU) in pre-exascalesystems, using self-hosted hardware; (ii) incremental reduction of memory leaks using sanitizers, (iii) incorporation of Dockercontainers for CI and reproducibility, and (iv) refactoring efforts to improve maintainability, testing coverage, and memory lifetime management. We quantify the value of these improvements by providing metrics to illustrate the shift towards a predictive, rather than reactive, sustainable maintenance approach. Our goal, in documenting the impact of these efforts on QMCPACK, is to contribute to the body of knowledge on the importance of research software engineering (RSE) for the sustainability of community HPC codes and scientific discovery at scale.

Godoy, William↗
DOE OSTI2023

Application Experiences on a GPU-Accelerated Arm-based HPC Testbed

This paper assesses and reports the experience of ten teams working to port, validate, and benchmark several High Performance Computing applications on a novel GPU-accelerated Arm testbed system. The testbed consists of eight NVIDIA Arm HPC Developer Kit systems, each one equipped with a server-class Arm CPU from Ampere Computing and two data center GPUs from NVIDIA Corp. The systems are connected together using InfiniBand interconnect. The selected applications and mini-apps are written using several programming languages and use multiple accelerator-based programming models for GPUs such as CUDA, OpenACC, and OpenMP offloading. Working on application porting requires a robust and easy-to-access programming environment, including a variety of compilers and optimized scientific libraries. The goal of this work is to evaluate platform readiness and assess the effort required from developers to deploy well-established scientific workloads on current and future generation Arm-based GPU-accelerated HPC systems. The reported case studies demonstrate that the current level of maturity and diversity of software and tools is already adequate for large-scale production deployments.

Elwasif, Wael↗
DOE OSTI2022

A High-Performance Design for Hierarchical Parallelism in the QMCPACK Monte Carlo code

We introduce a new high-performance design for parallelism within the Quantum Monte Carlo code QMCPACK. We demonstrate that the new design is better able to exploit the hierarchical parallelism of heterogeneous architectures compared to the previous GPU implementation. The new version is able to achieve higher GPU occupancy via the new concept of crowds of Monte Carlo walkers, and by enabling more host CPU threads to effectively offload to the GPU. The higher performance is expected to be achieved independent of the underlying hardware, significantly improving developer productivity and reducing code maintenance costs. Scientific productivity is also improved with full support for fallback to CPU execution when GPU implementations are not available or CPU execution is more optimal.

Luo, Ye↗
DOE OSTI2020

Performance Assessment of AFQMC Implementation (ECP Milestone 5.2 Report)

The auxiliary field quantum Monte Carlo algorithm in QMCPACK was ported to run on AMD GPUs using HIP. Initial performance measurements on early access hardware indicate a 4x slowdown compared to NVIDIA V100s. The slowdown was determined to be a result of suboptimal ROCM libraries for small matrix sizes coupled with some poorly performing hipified CUDA kernels. Through a preliminary analysis of HIP kernel performance we determined a strategy for pro ling and tuning kernels. We request that ROCM libraries are optimized for smaller problem sizes particularly for batched operations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Intercalation-Induced Reversible Electrochromic Behavior of Two-Dimensional Ti 3 C 2 T x MXene in Organic Electrolytes

MXenes, a large family of two-dimensional materials, have attracted tremendous attention due to their unique physical and chemical properties. Reversible ion intercalation between MXene layers allows modification of the optical, thermal, magnetic, and chemical properties. The electrochemical charge/discharge of MXenes in aqueous electrolytes was reported to lead to reversible electrochromic behavior. In this work, the electrochromic effect of semitransparent Ti 3 C 2 T x MXene film was probed by electrochemical intercalation of Li ions. Correspondingly, a peak shift of 100 nm was observed in the UV-vis spectrum. By combining in-situ Raman spectroscopy, in-situ X-ray diffraction, and density functional theory calculations, we show that the electrochromic shift is primarily due to the formation of robust O-Li bonds and the emerging bands induced changes of inter-band excitations. Finally, understanding the mechanism of electrochromic behavior in Ti 3 C 2 T x lays the foundations of designating 2D materials with durable, controllable, and efficient intercalation-induced electrochromic behaviors.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Basic Energy Sciences Exascale Requirements Review. An Office of Science review sponsored jointly by Advanced Scientific Computing Research and Basic Energy Sciences, November 3-5, 2015, Rockville, Maryland

Computers have revolutionized every aspect of our lives. Yet in science, the most tantalizing applications of computing lie just beyond our reach. The current quest to build an exascale computer with one thousand times the capability of today’s fastest machines (and more than a million times that of a laptop) will take researchers over the next horizon. The field of materials, chemical reactions, and compounds is inherently complex. Imagine millions of new materials with new functionalities waiting to be discovered — while researchers also seek to extend those materials that are known to a dizzying number of new forms. We could translate massive amounts of data from high precision experiments into new understanding through data mining and analysis. We could have at our disposal the ability to predict the properties of these materials, to follow their transformations during reactions on an atom-by-atom basis, and to discover completely new chemical pathways or physical states of matter. Extending these predictions from the nanoscale to the mesoscale, from the ultrafast world of reactions to long-time simulations to predict the lifetime performance of materials, and to the discovery of new materials and processes will have a profound impact on energy technology. In addition, discovery of new materials is vital to move computing beyond Moore’s law. To realize this vision, more than hardware is needed. New algorithms to take advantage of the increase in computing power, new programming paradigms, and new ways of mining massive data sets are needed as well. This report summarizes the opportunities and the requisite computing ecosystem needed to realize the potential before us. In addition to pursuing new and more complete physical models and theoretical frameworks, this review found that the following broadly grouped areas relevant to the U.S. Department of Energy (DOE) Office of Advanced Scientific Computing Research (ASCR) would directly affect the Basic Energy Sciences (BES) mission need. Simulation, visualization, and data analysis are crucial for advances in energy science and technology. Revolutionary mathematical, software, and algorithm developments are required in all areas of BES science to take advantage of exascale computing architectures and to meet data analysis, management, and workflow needs. In partnership with ASCR, BES has an emerging and pressing need to develop new and disruptive capabilities in data science. More capable and larger high-performance computing (HPC) and data ecosystems are required to support priority research in BES. Continued success in BES research requires developing the next-generation workforce through education and training and by providing sustained career opportunities.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Grain boundary stability and influence on ionic conductivity in a disordered perovskite -- a first-principles investigation of lithium lanthanum titanate

The origin of ionic conductivity in bulk lithium lanthanum titanate, a promising solid electrolyte for Li-ion batteries, has long been under debate, with experiments showing lower conductivity than predictions. Recent microscopy images show Type I and Type II grain boundaries. Using first-principles based calculations we find that experimentally observed Type I boundaries are more stable compared to the Type II grain boundaries, consistent with their observed relative abundance. Grain boundary stability appears to strongly anti-correlate with the field strength as well as the spatial extent of the space charge region. Ion migration is faster along Type II grain boundaries than across, consistent with recent experiments of increased conductivity when Type II densities were increased.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Delayed Update Algorithms for Quantum Monte Carlo Simulation on GPU: Extended Abstract

QMCPACK is open source scientific software designed to perform Quantum Monte Carlo (QMC) simulation, a first-principles method for describing many-body systems. The evaluation of each Monte Carlo move requires finding the determinant of a dense matrix in the wave functions. This calculation forms a key computational kernel in QMCPACK. After each accepted event, the wave function matrix undergoes a rank-one update to represent a single particle move within the system. The matrix inverse is updated via the Sherman-Morrison formula; occasionally, the explicit inverse must be recomputed to maintain numerical stability.; We propose an alternate approach to this kernel that utilizes QR factorization to maintain stability without refactorization. In addition, algorithms based on a novel delayed update scheme are explored in this effort. This strategy enables probability evaluation for multiple successive Monte Carlo moves, with application of accepted moves to the wave function matrix delayed until an event is denied, or a predetermined limit on acceptances p is reached. Accepted events grouped in this manner are then applied to the matrix en bloc with enhanced arithmetic intensity and computational efficiency. Delayed update Sherman-Morrison probability evaluation algorithms with CPU and GPU-accelerated implementations are tested, profiled, and analyzed. A QR based delayed update algorithm remains in development. Results are evaluated against existing methods for numerical stability and efficiency; emphasis is placed on large systems, for which acceleration is critical.

Computer Science↗
DOE OSTI2013

Density functional study of the structure, stability and oxygen reduction activity of ultrathin platinum nanowires

We used density functional theory to study the difference in the structure, stability and catalytic reactivity between ultrathin, 0.5–1.0 nm diameter, platinum nanotubes and nanowires. Model nanowires were formed by inserting an inner chain of platinum atoms in small diameter nanotubes. In this way more stable, non-hollow structures were formed. The difference in the electronic structure of platinum nanotubes and nanowires was examined by inspecting the density of surface states and band structure. Furthermore, reactivity toward the oxygen reduction reaction of platinum nanowires was assessed by studying the change in the chemisorption energies of oxygen, hydroxyl, and hydroperoxyl groups, induced by converting the nanotube models to nanowires. Both ultrathin platinum nanotubes and nanowires show distinct properties compared to bulk platinum. Single-wall nanotubes and platinum nanowires with diameters larger than 1 nm show promise for use as oxygen reduction catalysts.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2012

Theoretical Study of the Structure, Stability and Oxygen Reduction Activity of Ultrathin Platinum Nanowires

We use density functional theory to study the difference in the structure, stability and catalytic reactivity between ultrathin, 0.5- 1.0 nm diameter, platinum nanotubes and nanowires. Model nanowires were formed by inserting an inner chain of platinum atoms in small diameter nanotubes. In this way more stable, nonhollow structures were formed. The difference in the electronic structure of platinum nanotubes and nanowires was examined by inspecting the density of surface states and band structure. Furthermore, reactivity towards the oxygen reduction reaction of platinum nanowires was addressed by studying the change in the chemisorption energies of oxygen and hydroxyl groups, induced by inserting the inner chain of platinum atoms into the hollow nanotubes. Both ultrathin platinum nanotubes and nanowires show distinct properties compared to bulk platinum. Nanotubes with diameters larger than 1 nm show promise for use as oxygen reduction catalysts.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2012

Density Functional Theory Study of Oxygen Reduction Activity on Ultrathin Platinum Nanotubes

The structure, stability, and catalytic activity of a number of single- and double-wall platinum (n,m) nanotubes ranging in diameter from 0.3 to 2.0 nm were studied using plane-wave based density functional theory in the gas phase and water environment. The change in the catalytic activity toward the oxygen reduction reaction (ORR) with the size and chirality of the nanotube was studied by calculating equilibrium adsorption potentials for ORR intermediates and by constructing free energy diagrams in the ORR dissociative mechanism network. In addition, the stability of the platinum nanotubes is investigated in terms of electrochemical dissolution potentials and by determining the most stable state of the material as a function of pH and potential, as represented in Pourbaix diagrams. Our results show that the catalytic activity and the stability toward electrochemical dissolution depend greatly on the diameter and chirality of the nanotube. On the basis of the estimated overpotentials for ORR, we conclude that smaller, approximately 0.5 nm in diameter single-wall platinum nanotubes consistently show a huge, up to 400 mV larger overpotential than platinum, indicating very poor catalytic activity toward ORR. This is the result of substantial structural changes induced by the adsorption of any chemical species on these tubes. Single-wall n = m platinum nanotubes with a diameter larger than 1 nm have smaller ORR overpotentials than bulk platinum for up to 180 mV and thus show improved catalytic activity relative to bulk. We also predict that these nanotubes can endure the highest cell potentials but dissolution potentials are still for 110 mV lower than for the bulk, indicating a possible corrosion problem.

Environmental Molecular Sciences Laboratory↗