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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
NASA NTRS1995

Software packager user's guide

Software integration is a growing area of concern for many programmers and software managers because the need to build new programs quickly from existing components is greater than ever. This includes building versions of software products for multiple hardware platforms and operating systems, building programs from components written in different languages, and building systems from components that must execute on different machines in a distributed network. The goal of software integration is to make building new programs from existing components more seamless -- programmers should pay minimal attention to the underlying configuration issues involved. Libraries of reusable components and classes are important tools but only partial solutions to software development problems. Even though software components may have compatible interfaces, there may be other reasons, such as differences between execution environments, why they cannot be integrated. Often, components must be adapted or reimplemented to fit into another application because of implementation differences -- they are implemented in different programming languages, dependent on different operating system resources, or must execute on different physical machines. The software packager is a tool that allows programmers to deal with interfaces between software components and ignore complex integration details. The packager takes modular descriptions of the structure of a software system written in the package specification language and produces an integration program in the form of a makefile. If complex integration tools are needed to integrate a set of components, such as remote procedure call stubs, their use is implied by the packager automatically and stub generation tools are invoked in the corresponding makefile. The programmer deals only with the components themselves and not the details of how to build the system on any given platform.

Callahan, John R.↗
DOE OSTI2024

Simulating charged defects at database scale

Point defects have a strong influence on the physical properties of materials, often dominating the electronic and optical behavior in semiconductors and insulators. The simulation and analysis of point defects is, therefore, crucial for understanding the growth and operation of materials, especially for optoelectronics applications. In this work, we present a general-purpose Python framework for the analysis of point defects in crystalline materials as well as a generalized workflow for their treatment with high-throughput simulations. The distinguishing feature of our approach is an emphasis on a unique, unit cell, structure-only, definition of point defects which decouples the defect definition, and the specific supercell representation used to simulate the defect. This allows the results of first-principles calculations to be aggregated into a database without extensive provenance information and is a crucial step in building a persistent database of point defects that can grow over time, a key component toward realizing the idea of a “defect genome” that can yield more complex relationships governing the behavior of defects in materials. We demonstrate several examples of the approach for three technologically relevant materials and highlight current pitfalls that must be considered when employing these methodologies as well as their potential solutions.

36 MATERIALS SCIENCE↗
DOE OSTI2024

MolSym : A Python package for handling symmetry in molecular quantum chemistry

A consideration of the point group symmetry of molecules is often advantageous from a computational efficiency standpoint and sometimes necessary for the correct treatment of chemical physics problems. Many modern electronic structure software packages include a treatment of symmetry, but these are sometimes incomplete or unusable outside of that program’s environment. Therefore, we have developed the MolSym package for handling molecular symmetry and its associated functionalities to provide a platform for including symmetry in the implementation and development of other methods. Features include point group detection, molecule symmetrization, arbitrary generation of symmetry element sets and character tables, and symmetry adapted linear combinations of real spherical harmonic basis functions, Cartesian displacement coordinates, and internal coordinates. We present some of the advantages of using molecular symmetry as achieved by MolSym, particularly with respect to Hartree–Fock theory, and the reduction of finite difference displacements in gradient/Hessian computations. Furthermore, this package is designed to be easily integrated into other software development efforts and may be extended to further symmetry applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

cclib 2.0: An updated architecture for interoperable computational chemistry

Interoperability in computational chemistry is elusive, impeded by the independent development of software packages and idiosyncratic nature of their output files. The cclib library was introduced in 2006 as an attempt to improve this situation by providing a consistent interface to the results of various quantum chemistry programs. The shared API across programs enabled by cclib has allowed users to focus on results as opposed to output and to combine data from multiple programs or develop generic downstream tools. Initial development, however, did not anticipate the rapid progress of computational capabilities, novel methods, and new programs; nor did it foresee the growing need for customizability. Here, we recount this history and present cclib 2, focused on extensibility and modularity. We also introduce recent design pivots—the formalization of cclib’s intermediate data representation as a tree-based structure, a new combinator-based parser organization, and parsed chemical properties as extensible objects.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Integration of scanning probe microscope with high-performance computing: Fixed-policy and reward-driven workflows implementation

The rapid development of computation power and machine learning algorithms has paved the way for automating scientific discovery with a scanning probe microscope (SPM). The key elements toward operationalization of the automated SPM are the interface to enable SPM control from Python codes, availability of high computing power, and development of workflows for scientific discovery. Here, we build a Python interface library that enables controlling an SPM from either a local computer or a remote high-performance computer, which satisfies the high computation power need of machine learning algorithms in autonomous workflows. We further introduce a general platform to abstract the operations of SPM in scientific discovery into fixed-policy or reward-driven workflows. Furthermore, our work provides a full infrastructure to build automated SPM workflows for both routine operations and autonomous scientific discovery with machine learning.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

RANGE: A robust adaptive nature-inspired global explorer of potential energy surfaces

With the growing demand for realistic representations of chemical structures and the advent of exascale computing, the intelligent sampling of potential energy surfaces and efficient identification of global minima have become more essential but also more feasible. Building on prior studies demonstrating the efficiency of the Artificial Bee Colony (ABC) swarm intelligence algorithm, we report a hybrid metaheuristic framework that integrates the adaptive exploration capabilities of ABC coupled with the exploitation strengths of genetic algorithms (GA) in a scalable, Python-based implementation. The resulting tool, RANGE (Robust Adaptive Nature-inspired Global Explorer), provides seamless interfaces to multiple potential energy evaluators, either directly or via widely used Python libraries, and is designed for high-performance computing environments. We describe the implementation details of RANGE and evaluate its performance, relative to ABC- or GA-alone based algorithms, on a variety of chemical systems, including molecular clusters and heterogeneous surfaces. In conclusion, our results demonstrate RANGE’s efficiency, robustness, and broad applicability in addressing challenging global optimization problems in computational chemistry and materials science.

Algorithms and data structure↗
DOE OSTI2026

pyRMG: A framework for high-throughput, large-cell DFT calculations on supercomputers

Exascale computing delivers the raw power to simulate ever larger and more chemically realistic systems, but realizing this potential requires codes that can efficiently use thousands of processors. Our real-space multigrid (RMG) density functional theory (DFT) code’s grid-decomposition approach scales nearly linearly with the number of graphics processing units (GPUs), even for simulations exceeding thousands of atoms. This scalability makes RMG a compelling tool for high-throughput DFT studies of materials that would otherwise be bottlenecked in other codes (for example, by global fast Fourier transforms in plane-wave DFT). However, the limited workflow infrastructure for RMG has thus far constrained its adoption to a small user community. In this work, we present pyRMG, a Python package designed to streamline the setup and execution of RMG DFT calculations. Built on the pymatgen and ASE (Atomic Simulation Environment) computational materials science Python packages, pyRMG automates input generation and convergence checking, and it integrates with modern job schedulers (e.g., Flux) on leadership-class platforms such as Frontier and Perlmutter. Here, we demonstrate pyRMG for a high-throughput study of strain effects in 2D 2L-Bi 2 Se 3 /2L-NbSe 2 heterostructures, which offers chemical insights into this system and shows that RMG-based workflows can converge with limited user intervention.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Numerical simulation projects in micromagnetics with Jupyter

We report a case study where an existing materials science course was modified to include numerical simulation projects on the micromagnetic behavior of materials. The Ubermag micromagnetic simulation software package is used in order to solve problems computationally. The simulation software is controlled through the Python code in Jupyter notebooks. Our experience is that the self-paced problem-solving nature of the project work can facilitate a better in-depth exploration of the course contents. We discuss which aspects of the Ubermag and the project Jupyter ecosystem have been beneficial for the students' learning experience and which could be transferred to similar teaching activities in other subject areas.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1967

A user-oriented time-shared online system.

Computerized method for on-line data analysis, elucidating SLIP language, request, processing, interpretation, time sharing, background activity, etc

LANGUAGE PROGRAMMING↗
NASA NTRS1981

Developing experiment instrument packages

Ground-Support Equipment (GSE) system supports development, calibration, and testing of experiment packages. It is also used for "quick look" processing and in-progress data analysis. User interacts with incoming telemetry data, performs computations, and controls execution of procedures using versatile Experiment Command Interactive Language (ECIL). Program is implemented many ways with minimal modification. It is written in MARCO II and FORTRAN for DEC PDP-11/34 using the RSX-11M operating system.

Herreid, R.↗
NASA NTRS1982

I/O Error Analyzer (UNIVAC 1108 Version)

IOALZ4 is an Assembly-language utility program for UNIVAC 1108, operational under level 33 of EXEC 8 operating system. It scans user-selected portions of system log file, whether located on tape or mass storage, searching for and processing I/O error entries.

Vaughan, E. T.↗
NASA NTRS1982

NASTRAN: April 1982 Release

Latest public release of NASTRAN, April 1982 most efficient and versatile to date. Intended range of applications of NASTRAN includes almost every kind of structure and construction. Users may develop their own analysis capabilities by using Direct Matrix Abstraction Programming (DMAP) language to direct NASTRAN in solution of general matrix problems.

Brugh, R. L.↗
NASA NTRS1982

A determination of the current density in electron beams

Current gathering rotating probe techniques were used to examine the envelope shape and power density profile of electron beams used in electron beam welding devices. The electron power density contours which determine the shape of the weld vapor cavity, penetration, and local heat distribution were considered. A mathematical analysis consistent with a rotating probe technique necessary to determine the current density distribution (assumed symmetrically radial) in a cross-section of the beam is provided. An explanation of the experimental technique for obtaining data, a BASIC language computer program to determine the current density from the data, and a study indicating the level of confidence to be associated with results obtained are also provided. An example of the application of the analysis to some experimental electron beam data is included.

Beil, R. J.↗