Engineering topics

Kent, Paul R.C.

Publications and source records attributed to Kent, Paul R.C..

4 records

DOE OSTI2024

Software stewardship and advancement of a high-performance computing scientific application: QMCPACK

Here, we provide an overview of the software engineering efforts and their impact in QMCPACK, a production-level ab-initio Quantum Monte Carlo open-source code targeting high-performance computing (HPC) systems. Aspects included are: (i) strategic expansion of continuous integration (CI) targeting CPUs, using GitHub Actions own runners, and NVIDIA and AMD GPUs used in pre-exascale systems, (ii) incremental reduction of memory leaks using sanitizers, (iii) incorporation of Docker containers 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, 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 stewardship and advancement of community HPC codes to enable scientific discovery at scale.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

OpenMP application experiences: Porting to accelerated nodes

As recent enhancements to the OpenMP specification become available in its implementations, there is a need to share the results of experimentation in order to better understand the OpenMP implementation’s behavior in practice, to identify pitfalls, and to learn how the implementations can be effectively deployed in scientific codes. We report on experiences gained and practices adopted when using OpenMP to port a variety of ECP applications, mini-apps and libraries based on different computational motifs to accelerator-based leadership-class high-performance supercomputer systems at the United States Department of Energy. Additionally, we identify important challenges and open problems related to the deployment of OpenMP. Through our report of experiences, we find that OpenMP implementations are successful on current supercomputing platforms and that OpenMP is a promising programming model to use for applications to be run on emerging and future platforms with accelerated nodes.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Interfacial charge transfer and interaction in the MXene/TiO 2 heterostructures

Hybrid materials of MXenes [two-dimensional (2D) carbides and nitrides] and transition-metal oxides have shown great promise in electrical energy storage (EES) and 2D heterostructures have been proposed as the next-generation electrode materials to expand the limits of current technology. Here we use first principles density functional theory to investigate the interfacial structure, energetics, and electronic properties of the heterostructures of MXenes (Ti n+1 C n T 2 ; T = terminal groups) and anatase TiO 2 . We find that the greatest work-function differences are between OH-terminated MXene (1.6 eV) and anatase TiO 2 (101) (6.4 eV), resulting in the largest interfacial electron transfer (~0.9e/nm 2 across the interface) from MXene to the TiO 2 layer. This interface also has the strongest adhesion and is further strengthened by hydrogen bond formation. For O–, F–, or mixed O–/F– terminated Ti n+1 C n MXenes, electron transfer is minimal and interfacial adhesion is weak for their heterostructures with TiO 2 . The strong dependence of the interfacial properties of the MXene/TiO 2 heterostructures on the surface chemistry of the MXenes will be useful to tune the heterostructures for EES applications.

2-dimensinal systems↗

DOE OSTI2020

A combined machine learning and density functional theory study of binary Ti-Nb and Ti-Zr alloys: Stability and Young’s modulus

The multicomponent Ti alloys, specifically the -phase, have experienced a strong growth over the last decades, due to their outstanding properties of ultra-high strength and low Young’s modulus. These properties play a significant role in many aerospace and biomedical applications. Selection and optimization of multicomponent alloys is challenging due to the vast chemical and compositional space. Here we investigate the use of machine learning techniques informed by density functional calculations to guide the selection of Nb- and Zr-based Ti binary alloys. From the cubic structures obtained from high throughput calculations and literature, we identify several structures with Young’s moduli below 40 GPa. The multivariant decision tree methods provide efficient surrogate models to identify structure variables have high influences on the energetic stability and Young’s modulus. Furthermore, we implement a workflow of incorporating DFT provided results and machine learning method to explore the chemical and composition space of other binary and multicomponent alloys, to eventually accelerate the material design via taking advantages of identified key variables.

36 MATERIALS SCIENCE↗