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All sourcesNASA NTRSDOE OSTI
DOE OSTI2014

Analyzing the errors of DFT approximations for compressed water systems

We report an extensive study of the errors of density functional theory (DFT) approximations for compressed water systems. The approximations studied are based on the widely used PBE and BLYP exchange-correlation functionals, and we characterize their errors before and after correction for 1- and 2-body errors, the corrections being performed using the methods of Gaussian approximation potentials. The errors of the uncorrected and corrected approximations are investigated for two related types of water system: first, the compressed liquid at temperature 420 K and density 1.245 g/cm{sup 3} where the experimental pressure is 15 kilobars; second, thermal samples of compressed water clusters from the trimer to the 27-mer. For the liquid, we report four first-principles molecular dynamics simulations, two generated with the uncorrected PBE and BLYP approximations and a further two with their 1- and 2-body corrected counterparts. The errors of the simulations are characterized by comparing with experimental data for the pressure, with neutron-diffraction data for the three radial distribution functions, and with quantum Monte Carlo (QMC) benchmarks for the energies of sets of configurations of the liquid in periodic boundary conditions. The DFT errors of the configuration samples of compressed water clusters are computed using QMC benchmarks. We find that the 2-body and beyond-2-body errors in the liquid are closely related to similar errors exhibited by the clusters. For both the liquid and the clusters, beyond-2-body errors of DFT make a substantial contribution to the overall errors, so that correction for 1- and 2-body errors does not suffice to give a satisfactory description. For BLYP, a recent representation of 3-body energies due to Medders, Babin, and Paesani [J. Chem. Theory Comput. 9, 1103 (2013)] gives a reasonably good way of correcting for beyond-2-body errors, after which the remaining errors are typically 0.5 mE{sub h} ≃ 15 meV/monomer for the liquid and the clusters.

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

Progress toward Accelogic compression in ROOT

For the last 7 years, Accelogic pioneered and perfected a radically new theory of numerical computing codenamed “Compressive Computing”, which has an extremely profound impact on real-world computer science [1]. At the core of this new theory is the discovery of one of its fundamental theorems which states that, under very general conditions, the vast majority (typically between 70% and 80%) of the bits used in modern large-scale numerical computations are absolutely irrelevant for the accuracy of the end result. This theory of Compressive Computing provides mechanisms able to identify (with high intelligence and surgical accuracy) the number of bits (i.e., the precision) that can be used to represent numbers without affecting the substance of the end results, as they are computed and vary in real time. The bottom-line outcome will be to provide state-of-the-art compression algorithms --and accompanying software libraries-- able to surpass the performance of the compression engines currently available in the ROOT [7] framework. The resulting technology has the capability to enable substantial economic and operational gains (including speedup) for High Energy and Nuclear Physics data storage/analysis. In our initial studies, a factor of nearly x4 (3.9) compression was achieved with RHIC/STAR data where ROOT compression managed only x1.4 [6].As a collaboration of experimental scientists, private industry, and the ROOT Team, our aim is to capitalize on the substantial success delivered by the initial effort and produce a robust technology properly packaged as an open-source tool that could be used by virtually every experiment around the world as means for improving data management and accessibility.In this contribution, we will present our efforts integrating our concepts of “functionally lossless compression” within the ROOT framework implementation, with the purpose of producing a basic solution readily integrated into HENP applications. We will also present our progress applying this compression through realistic examples of analysis from both the STAR and CMS experiments.

Canal, Ph.↗
DOE OSTI2014

In-situ neutron diffraction of LaCoO₃ perovskite under uniaxial compression. I. Crystal structure analysis and texture development

The dynamics of texture formation, changes in crystal structure, and stress accommodation mechanisms have been studied in perovskite-type R3⁻c rhombohedral LaCoO₃ during uniaxial compression using in-situ neutron diffraction. The in-situ neutron diffraction revealed the complex crystallographic changes causing the texture formation and significant straining along certain crystallographic directions during compression, which are responsible for the appearance of hysteresis and non-linear ferroelastic deformation in the LaCoO₃ perovskite. The irreversible strain after the first loading was connected with the appearance of non-recoverable changes in the intensity ratio of certain crystallographic peaks, causing non-reversible texture formation. However, in the second loading/unloading cycle, the hysteresis loop was closed and no further irrecoverable strain appeared after deformation. The significant texture formation is responsible for an increase in the Young's modulus of LaCoO₃ at high compressive stresses, ranging from 76 GPa at the very beginning of the loading to 194 GPa at 900 MPa at the beginning of the unloading curve.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2017

Compressive Classification for TEM-EELS

Acquiring atomic-resolution images of beam sensitive materials or under dynamic conditions (e.g. operando STEM) is very difficult and requires precise dose fractioning to minimize the dose delivered to the sample and acquire the data more rapidly than dynamics present. Typical methods of low-dose STEM imaging include adjusting the beam current, dwell-time, and probe Scanning transmission electron microscopy (STEM) is used routinely to obtain atomic-resolution images of a small selection of radiation tolerant materials under stable conditions.size. All three of these determine the dose over a spatial area in STEM. Recently, compressive sensing [1] has been applied to electron microscopy. The computational imaging method of inpainting [2] has been proposed as a method to reduce dose and increase acquisition speed for STEM. Inpainting is a technique to recover missing pixels in an image. By skipping pixels (randomly) during acquisition, a fourth dose-control mechanism is realized [3]. A few implementations of randomly-subsampled STEM exist (e.g., [4]). To make the concepts of compressive sensing accessible to the microscopy community we begin with an example using atomic resolution images of a GaAs crystal (see Figure 1). Because the crystal is periodic it has a sparse representation in reciprocal space. In fact, a sparse representation is a key component of compressive sensing. If an image is sub-sampled randomly we find that the Fourier transform remains sparse, but becomes increasing noisy as less and less data is acquired. Yet, by finding the peaks in the reciprocal image and removing the noise it is possible to recover atomic resolution images with only 1% of the pixels. Figure 1 shows the reconstruction process for GaAs sub-sampled at 50%, 10%, 5%, and 1%. Periodic images form a special class for which the optimal sampling approach (according to CS theory) is a random selection of pixels. For non-periodic images, such as those arising in in-situ STEM, methods such as dictionary learning are necessary [2,5]. The data can be compressed in time for TEM [5] or pixels can be skipped randomly for STEM. There are several issues that arise in developing a random scan for STEM [4] and a coded aperture for TEM. The remainder of the talk will address these issues. References: [1] E Candes and M Wakin. Signal Processing Magazine, IEEE. 2(25) (2008), pp. 21. [2] A Stevens, Pu Y, Sun Y, et al. Artificial Intelligence & Statistics (AISTATS), 2017. [3] A Stevens, H Yang, L Carin et al. Microscopy 63(1), (2014), pp. 41. [4] L Kovarik, A Stevens, A Liyu et al. Appl. Phys. Lett. 109, 164102 (2016). [5] A Stevens, L Kovarik, P Abellan et al. Adv. Structural and Chemical Imaging 1(10), (2015), pp. 1. [6] Supported by the Chemical Imaging, Signature Discovery, and Analytics in Motion Initiatives at PNNL. PNNL is operated by Battelle Memorial Inst. for the US DOE; contract DE-AC05-76RL01830.

Hao, Weituo↗
DOE OSTI2017

Compression Ratio Ion Mobility Programming (CRIMP) Accumulation and Compression of Billions of Ions for Ion Mobility-Mass Spectrometry Using Traveling Waves in Structures for Lossless Ion Manipulations (SLIM)

We report on the implementation of a traveling wave (TW) based compression ratio ion mobility programming (CRIMP) approach within Structures for Lossless Ion Manipulations (SLIM) that enables both greatly enlarged trapped ion charge capacities and also their subsequent efficient compression for use in ion mobility (IM) separations. Ion accumulation is conducted in a long serpentine path TW SLIM region after which CRIMP allows the large ion populations to be ‘squeezed’. The compression process occurs at an interface between two SLIM regions, one operating conventionally and the second having an intermittently pausing or ‘stuttering’ TW, allowing the contents of multiple bins of ions from the first region to be merged into a single bin in the second region. In this initial work stationary voltages in the second region were used to block ions from exiting the first (trapping) region, and the resumption of TWs in the second region allows ions to exit, and the population to also be compressed if CRIMP is applied. In our initial evaluation we show that the number of charges trapped for a 40 s accumulation period was ~5×109, more than two orders of magnitude greater than the previously reported charge capacity using an ion funnel trap. We also show that over 1×109 ions can be accumulated with high efficiency in the present device, and that the extent of subsequent compression is only limited by the space charge capacity of the trapping region. Lower compression ratios allow increased IM peak heights without significant loss of signal, while excessively large compression ratios can lead to ion losses and other artifacts. Importantly, we show that extended ion accumulation in conjunction with CRIMP and multiple passes provides the basis for a highly desirable combination of ultra-high sensitivity and ultra-high resolution IM separations using SLIM.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2018

In situ X-Ray Diffraction of Shock-Compressed Fused Silica

Because of its widespread applications in materials science and geophysics, SiO 2 has been extensively examined under shock compression. Both quartz and fused silica transform through a so-called “mixed-phase region” to a dense, low compressibility high-pressure phase. For decades, the nature of this phase has been a subject of debate. Proposed structures include crystalline stishovite, another high-pressure crystalline phase, or a dense amorphous phase. Here we use plate-impact experiments and pulsed synchrotron x-ray diffraction to examine the structure of fused silica shock compressed to 63 GPa. In contrast to recent laser-driven compression experiments, we find that fused silica adopts a dense amorphous structure at 34 GPa and below. When compressed above 34 GPa, fused silica transforms to untextured polycrystalline stishovite. Our results can explain previously ambiguous features of the shock-compression behavior of fused silica and are consistent with recent molecular dynamics simulations. Stishovite grain sizes are estimated to be ~5–30 nm for compression over a few hundred nanosecond time scale.

58 GEOSCIENCES↗
DOE OSTI2025

Eureka: Enabling Fine-Grained Access and Range Queries on Compressed Scientific Data via Data-Index Co-Compression

Handling large-scale scientific data in high-performance computing (HPC) environments poses significant challenges, including excessive I/O, high storage costs, and slow query performance. Traditional approaches often require full data decompression and scans, making them impractical for real-time or interactive analysis. To address these limitations, we introduce Eureka, a unified data-index co-compression framework that enables fine-grained access and efficient range queries on compressed scientific datasets. Eureka integrates spatial domain decomposition with block-wise error-bounded lossy compression to support selective decompression. It constructs a hierarchical AVL-tree index during compression to capture block-level value ranges, enabling fast pruning during query execution. To reduce metadata overhead, the index itself is also compressed while ensuring recall-preserving results. Experiments on six diverse HPC simulation datasets show that Eureka achieves up to 25x data compression and over 300x index compression, surpassing state-of-the-art compressors such as SZ3 and ZFP in rate-distortion performance. Additionally, Eureka delivers over 30x speedup for low-selectivity range queries, making it a scalable and efficient solution for modern scientific data analysis.

Yan, Ning↗
DOE OSTI2015

Strength and texture of Pt compressed to 63 GPa

Angle- and energy-dispersive X-ray diffraction experiments in a radial geometry were performed in the diamond anvil cell on polycrystalline platinum samples at pressures up to 63 GPa. Observed yield strength and texture depend on grain size. For samples with 70–300-nm particle size, the yield strength is 5–6 GPa at ∼60 GPa. Coarse-grained (∼2-μm particles) Pt has a much lower yield strength of 1–1.5 GPa at ∼60 GPa. Face-centered cubic metals Pt and Au have lower strength to shear modulus ratio than body-centered cubic or hexagonal close-packed metals. While a 300-nm particle sample exhibits the 〈110〉 texture expected of face-centered-cubic metals under compression, smaller and larger particles show a weak mixed 〈110〉 and 〈100〉 texture under compression. Differences in texture development may also occur due to deviations from uniaxial stress under compression in the diamond anvil cell.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2017

Thermodynamic parameters of helium under shock-wave and quasi-isentropic compressions at pressures up to 4800 GPa and compression ratios up to 900

The thermodynamic parameters of a strongly nonideal helium plasma obtained in experimental devices of hemispherical and spherical geometries are presented. Under shock-wave loading in the hemispherical device, the helium plasma was compressed to a density ρ ≈ 0.76 g cm{sup –3} by a pressure P ≈ 83 GPa at a temperature T ≈ 51000 K. Two-cascade spherical experimental devices of two types were used under quasi-isentropic helium plasma compression. In the devices of the first type at the same initial gas pressure in both cavities of the shells, the helium plasma was compressed approximately by a factor of 200 to a density ρ ≈ 8 g cm{sup –3} by a pressure P ≈ 4800 GPa. In the devices of the second type at a ratio of the initial gas pressures in the cavities of about 9: 1, the thermodynamic parameters of a nonideal helium plasma compressed by a factor of 900 to a density ρ ≈ 5 g cm{sup –3} by a pressure P ≈ 3700 GPa were determined. The compressed-plasma pressure was determined from the results of gasdynamic computations. An X-ray radiograph consisting of three betatrons and a multichannel optoelectronic X-ray imaging system was used to determine the positions of the boundaries of the gaseous-helium-compressing steel shell.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Extreme Compression for Large Scale Data Store

For the last 5 years Accelogic pioneered and perfected a radically new theory of numerical computing codenamed “Compressive Computing”, which has an extremely profound impact on real-world computer science [1]. At the core of this new theory is the discovery of one of its fundamental theorems which states that, under very general conditions, the vast majority (typically between 70% and 80%) of the bits used in modern large-scale numerical computations are absolutely irrelevant for the accuracy of the end result. This theory of Compressive Computing provides mechanisms able to identify (with high intelligence and surgical accuracy) the number of bits (i.e., the precision) that can be used to represent numbers without affecting the substance of the end results, as they are computed and vary in real time. The bottom line outcome would be to provide a state-of-the-art compression algorithm that surpasses those currently available in the ROOT framework, with the purpose of enabling substantial economic and operational gains (including speedup) for High Energy and Nuclear Physics data storage/analysis. In our initial studies, a factor of nearly x4 (3.9) compression was achieved with RHIC/STAR data where ROOT compression managed only x1.4.In this contribution, we will present our concepts of “functionally lossless compression”, have a glance at examples and achievements in other communities, present the results and outcome of our current, ongoing R&D, as well as present a high-level view of our plan to move forward with a ROOT implementation that would deliver a basic solution readily integrated into HENP applications. As a collaboration of experimental scientists, private industry, and the ROOT Team, our aim is to capitalize on the substantial success delivered by the initial effort and produce a robust technology properly packaged as an open-source tool that could be used by virtually every experiment around the world as means for improving data management and accessibility.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2015

Development of a broadband reflectivity diagnostic for laser driven shock compression experiments

Here, a normal-incidence visible and near-infrared shock wave optical reflectivity diagnostic was constructed to investigate changes in the optical properties of materials under dynamic laser compression. Documenting wavelength- and time-dependent changes in the optical properties of laser-shock compressed samples has been difficult, primarily due to the small sample sizes and short time scales involved, but we succeeded in doing so by broadening a series of time delayed 800-nm pulses from an ultrafast Ti:sapphire laser to generate high-intensity broadband light at nanosecond time scales. This diagnostic was demonstrated over the wavelength range 450–1150 nm with up to 16 time displaced spectra during a single shock experiment. Simultaneous off-normal incidence velocity interferometry (velocity interferometer system for any reflector) characterized the sample under laser-compression and also provided an independent reflectivity measurement at 532 nm wavelength. The shock-driven semiconductor-to-metallic transition in germanium was documented by the way of reflectivity measurements with 0.5 ns time resolution and a wavelength resolution of 10 nm.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2015

On the order-disorder transition of compressible diblock copolymer melts

We performed both the fast off-lattice Monte Carlo simulations of symmetric diblock copolymers (DBC) in an isothermal-isobaric ensemble and the self-consistent field calculations of asymmetric DBC to properly determine the order-disorder transition (ODT) of a model system of compressible DBC melts used in the literature when it is a first-order phase transition, and studied for the first time the co-existence of the two phases at ODT. We found that the co-existing region is quite small and decreases as the system becomes less compressible, which justifies the previous ODT results obtained by equating the Helmholtz free energy per chain of the two phases. We also found that for the most compressible system where there is no repulsion between the same type of segments, the self-consistent field theory predicts that ODT is a second-order phase transition even for asymmetric DBC melts due to its mean-field approximation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗