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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Complexity of many-body interactions in transition metals via machine-learned force fields from the TM23 data set

Abstract This work examines challenges associated with the accuracy of machine-learned force fields (MLFFs) for bulk solid and liquid phases ofd-block elements. In exhaustive detail, we contrast the performance of force, energy, and stress predictions across the transition metals for two leading MLFF models: a kernel-based atomic cluster expansion method implemented using sparse Gaussian processes (FLARE), and an equivariant message-passing neural network (NequIP). Early transition metals present higher relative errors and are more difficult to learn relative to late platinum- and coinage-group elements, and this trend persists across model architectures. Trends in complexity of interatomic interactions for different metals are revealed via comparison of the performance of representations with different many-body order and angular resolution. Using arguments based on perturbation theory on the occupied and unoccupieddstates near the Fermi level, we determine that the large, sharpddensity of states both above and below the Fermi level in early transition metals leads to a more complex, harder-to-learn potential energy surface for these metals. Increasing the fictitious electronic temperature (smearing) modifies the angular sensitivity of forces and makes the early transition metal forces easier to learn. This work illustrates challenges in capturing intricate properties of metallic bonding with current leading MLFFs and provides a reference data set for transition metals, aimed at benchmarking the accuracy and improving the development of emerging machine-learned approximations.

Chemistry↗
DOE OSTI2021

Materials Fingerprinting Classification

Significant progress in many classes of materials could be made with the availability of experimentally-derived large datasets composed of atomic identities and three-dimensional coordinates. Methods for visualizing the local atomic structure, such as atom probe tomography (APT), which routinely generate datasets comprised of millions of atoms, are an important step in realizing this goal. However, state-of-the-art APT instruments generate noisy and sparse datasets that provide information about elemental type, but obscure atomic structures, thus limiting their subsequent value for materials discovery. The application of a materials fingerprinting process, a machine learning algorithm coupled with topological data analysis, provides an avenue by which here-to-fore unprecedented structural information can be extracted from an APT dataset. As a proof of concept, the material fingerprint is applied to high-entropy alloy APT datasets containing body-centered cubic (BCC) and face-centered cubic (FCC) crystal structures. A local atomic configuration centered on an arbitrary atom is assigned a topological descriptor, with which it can be characterized as a BCC or FCC lattice with near perfect accuracy, despite the inherent noise in the dataset. Finally, this successful identification of a fingerprint is a crucial first step in the development of algorithms which can extract more nuanced information, such as chemical ordering, from existing datasets of complex materials.

36 MATERIALS SCIENCE↗
DOE OSTI2021

pyDRESCALk

Modern data scientists are tasked to analyze ever-growing data sets with increasingly complex relationships. Tensor decompositions have come to play a central role in identifying underlying latent structures in higher-order data. The problem of fitting tensor models to different distributions is complicated by the combinations of size, dimensionality, and sparsity present in real world data. The situation demands efficient algorithms designed for shared-memory and distributed systems. This work will present new research that tackles these challenges on several different fronts, leveraging optimizations in numerical algorithms and sparse tensor representations in heterogeneous high performance computing environments.

Bhattarai, Manish↗
DOE OSTI2023

Cloud and Precipitation Experiment at Kennaook (Cape-K) Science Plan

The Cloud and Precipitation Experiment at Kennaook (CAPE-K) will augment ongoing measurements at the Kennaook/Cape Grim Baseline Air Pollution Station (KCG) with components of the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility’s second Mobile Facility (AMF2) in a deployment that is scheduled to extend from April 2024 through September 2025. Located at Kennaook/Cape Grim on the northwestern tip of Tasmania (40.68° S, 144.69° E), KCG has produced the longest and most consequential record of Southern Hemisphere aerosol and gas-phase chemistry; however, extensive cloud and precipitation measurements have not been collected at this site. CAPE-K is motivated by the fact that model uncertainty in the surface radiation budget due to clouds and precipitation remain significant in this region, where strong latitudinal gradients in cloud feedbacks are found and there are strong and well-documented seasonal variations in marine aerosol properties. Satellite data suggest that cloud properties co-vary with the seasonal cycle in aerosol in the Southern Ocean (SO), yet detailed vertically resolved measurements of cloud and precipitation in the marine boundary layer are sparse. CAPE-K will provide the first such seasonal cycle of measurements in this important latitude band. CAPE-K will provide detailed cloud and precipitation observations over two winter seasons and align with complementary high-value-add activities planned by Australian colleagues, including a voyage of the Research Vessel Investigator (RVI) in May 2025 that will conduct measurements just offshore of KCG.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Modeling Spatial Distribution of Snow Water Equivalent by Combining Meteorological and Satellite Data with Lidar Maps

Abstract An accurate characterization of the water content of snowpack, or snow water equivalent (SWE), is necessary to quantify water availability and constrain hydrologic and land surface models. Recently, airborne observations (e.g., lidar) have emerged as a promising method to accurately quantify SWE at high resolutions (scales of ∼100 m and finer). However, the frequency of these observations is very low, typically once or twice per season in the Rocky Mountains of Colorado. Here, we present a machine learning framework that is based on random forests to model temporally sparse lidar-derived SWE, enabling estimation of SWE at unmapped time points. We approximated the physical processes governing snow accumulation and melt as well as snow characteristics by obtaining 15 different variables from gridded estimates of precipitation, temperature, surface reflectance, elevation, and canopy. Results showed that, in the Rocky Mountains of Colorado, our framework is capable of modeling SWE with a higher accuracy when compared with estimates generated by the Snow Data Assimilation System (SNODAS). The mean value of the coefficient of determination R 2 using our approach was 0.57, and the root-mean-square error (RMSE) was 13 cm, which was a significant improvement over SNODAS (mean R 2 = 0.13; RMSE = 20 cm). We explored the relative importance of the input variables and observed that, at the spatial resolution of 800 m, meteorological variables are more important drivers of predictive accuracy than surface variables that characterize the properties of snow on the ground. This research provides a framework to expand the applicability of lidar-derived SWE to unmapped time points. Significance Statement Snowpack is the main source of freshwater for close to 2 billion people globally and needs to be estimated accurately. Mountainous snowpack is highly variable and is challenging to quantify. Recently, lidar technology has been employed to observe snow in great detail, but it is costly and can only be used sparingly. To counter that, we use machine learning to estimate snowpack when lidar data are not available. We approximate the processes that govern snowpack by incorporating meteorological and satellite data. We found that variables associated with precipitation and temperature have more predictive power than variables that characterize snowpack properties. Our work helps to improve snowpack estimation, which is critical for sustainable management of water resources.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Sparse regression for plasma physics

Many scientific problems can be formulated as sparse regression, i.e., regression onto a set of parameters when there is a desire or expectation that some of the parameters are exactly zero or do not substantially contribute. This includes many problems in signal and image processing, system identification, optimization, and parameter estimation methods such as Gaussian process regression. Sparsity facilitates exploring high-dimensional spaces while finding parsimonious and interpretable solutions. In the present work, we illustrate some of the important ways in which sparse regression appears in plasma physics and point out recent contributions and remaining challenges to solving these problems in this field. Further, a brief review is provided for the optimization problem and the state-of-the-art solvers, especially for constrained and high-dimensional sparse regression.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2013

WFC3 GRISM CONFIRMATION OF THE DISTANT CLUSTER Cl J1449+0856 AT (z) = 2.00: QUIESCENT AND STAR-FORMING GALAXY POPULATIONS

We present deep Hubble Space Telescope Wide Field Camera 3 (HST/WFC3) slitless spectroscopic observations of the distant cluster Cl J1449+0856. These cover a single pointing with 18 orbits of G141 spectroscopy and F140W imaging, allowing us to derive secure redshifts down to M{sub 140} ∼ 25.5 AB and 3σ line fluxes of ∼5 × 10{sup –18} erg s{sup –1} cm{sup –2}. In particular, we were able to spectroscopically confirm 12 early-type galaxies (ETGs) in the field up to z ∼ 3, 6 of which are in the cluster core, which represents the first direct spectroscopic confirmation of quiescent galaxies in a z = 2 cluster environment. With 140 redshifts in a ∼6 arcmin{sup 2} field, we can trace the spatial and redshift galaxy distribution in the cluster core and background field. We find two strong peaks at z = 2.00 and z = 2.07, where only one was seen in our previously published ground-based data. Due to the spectroscopic confirmation of the cluster ETGs, we can now reevaluate the redshift of Cl J1449+0856 at z = 2.00, rather than z = 2.07, with the background overdensity being revealed to be sparse and {sup s}heet{sup -}like. This presents an interesting case of chance alignment of two close yet unrelated structures, each one preferentially selected by different observing strategies. With 6 quiescent or early-type spectroscopic members and 20 star-forming ones, Cl J1449+0856 is now reliably confirmed to be at z = 2.00. The identified members can now allow for a detailed study of galaxy properties in the densest environment at z = 2.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Neural Networks to Find the Optimal Forcing for Offsetting the Anthropogenic Climate Change Effects

Abstract Of great relevance to climate engineering is the systematic relationship between the radiative forcing to the climate system and the response of the system, a relationship often represented by the linear response function (LRF) of the system. However, estimating the LRF often becomes an ill-posed inverse problem due to high-dimensionality and nonunique relationships between the forcing and response. Recent advances in machine learning make it possible to address the ill-posed inverse problem through regularization and sparse system fitting. Here, we develop a convolutional neural network (CNN) for regularized inversion. The CNN is trained using the surface temperature responses from a set of Green’s function perturbation experiments as imagery input data together with data sample densification. The resulting CNN model can infer the forcing pattern responsible for the temperature response from out-of-sample forcing scenarios. This promising proof of concept suggests a possible strategy for estimating the optimal forcing to negate certain undesirable effects of climate change. The limited success of this effort underscores the challenges of solving an inverse problem for a climate system with inherent nonlinearity. Significance Statement Predicting the climate response for a given climate forcing is a direct problem, while inferring the forcing for a given desired climate response is often an inverse, ill-posed, problem, posing a new challenge to the climate community. This study makes the first attempt to infer the radiative forcing for a given target pattern of global surface temperature response using a deep learning approach. The resulting deeply trained convolutional neural network inversion model shows promise in capturing the forcing pattern corresponding to a given surface temperature response, with a significant implication on the design of an optimal solar radiation management strategy for curbing global warming. This study also highlights the technical challenges that future research should prioritize in seeking feasible solutions to the inverse climate problem.

Ren, Huiying↗
DOE OSTI2017

Final report of a scalable, automated, semipermanent seismic array (SASSA) method for detecting CO 2 extent during geologic CO 2 injection

The scalable, automated, semipermanent seismic array (SASSA) project led and managed by the Energy & Environmental Research Center (EERC) was designed as a 3-year proof-of-concept study to evaluate and demonstrate an innovative application of the seismic method. The concept was to use a sparse surface array of 96 nodal seismic sensors paired with a single, remotely operated active seismic source at a fixed location to monitor for CO 2 saturation changes in a subsurface reservoir by processing the data for time-lapse changes at individual, strategically chosen reservoir reflection points. The combination of autonomous equipment and modern processing algorithms was used to apply the seismic method in a manner different from the normal paradigm of collecting a spatially dense data set to produce an image. It was used instead to monitor individual, strategically chosen reservoir reflection points for detectable signal character changes that could be attributed to the passing of a CO 2 saturation front or, possibly, changes in reservoir pressure. Data collection occurred over the course of 1 year at an oil field undergoing CO 2 injection for enhanced oil recovery (EOR) and focused on four overlapping “five-spot” EOR injector–producer patterns. Selection, procurement, configuration, installation, and testing of project equipment and collection of five baseline data sets were completed in advance of CO 2 injection within the study area. Weekly remote data collection produced 41 incremental time-lapse records for each of the 96 nodes. Validation was provided by two methods: 1) a conventional 2-D seismic line acquired through the center of the study area before injection started and again after the project ended and processed in a time-lapse manner and 2) by CO 2 saturation maps created from reservoir simulations based on injection and production history matching. Interpreted results were encouraging but mixed, with indications of changes likely due to the presence of CO 2 on some node reflection points where and when effects would be expected and noneffects where no CO 2 was expected, while results at some locations where simulation outputs suggested CO 2 should be present were ambiguous. Acquisition noise impacted interpretation of data at several locations. Many lessons learned were generated by the study to inform and improve results on a follow-up study. The ultimate aim of the project was to evaluate whether deployment of a SASSA technology can provide a useful and cost-effective monitoring solution for future CO 2 injection projects. The answer appears to be affirmative, with the expectation that lessons learned applied to future iterations, together with technology advances, will likely result in significant improvements.

58 GEOSCIENCES↗
DOE OSTI2023

symPACK: A GPU-Capable Fan-Out Sparse Cholesky Solver

Sparse symmetric positive definite systems of equations are ubiquitous in scientific workloads and applications. Parallel sparse Cholesky factorization is the method of choice for solving such linear systems. Therefore, the development of parallel sparse Cholesky codes that can efficiently run on today’s large-scale heterogeneous distributed-memory platforms is of vital importance. Modern supercomputers offer nodes that contain a mix of CPUs and GPUs. To fully utilize the computing power of these nodes, scientific codes must be adapted to offload expensive computations to GPUs. We present symPACK, a GPU-capable parallel sparse Cholesky solver that uses one-sided communication primitives and remote procedure calls provided by the UPC++ library. We also utilize the UPC++ "memory kinds" feature to enable efficient communication of GPU-resident data. We show that on a number of large problems, symPACK outperforms comparable state-of-the-art GPU-capable Cholesky factorization codes by up to 14x on the NERSC Perlmutter supercomputer.

Bellavita, Julian↗
DOE OSTI2024

Weak-form latent space dynamics identification

Recent work in data-driven modeling has demonstrated that a weak formulation of model equations enhances the noise robustness of a wide range of computational methods. In this paper, we demonstrate the power of the weak form to enhance the LaSDI (Latent Space Dynamics Identification) algorithm, a recently developed data-driven reduced order modeling technique. We introduce a weak form-based version WLaSDI (Weak-form Latent Space Dynamics Identification). WLaSDI first compresses data, then projects onto the test functions and learns the local latent space models. Notably, WLaSDI demonstrates significantly enhanced robustness to noise. With WLaSDI, the local latent space is obtained using weak-form equation learning techniques. Compared to the standard sparse identification of nonlinear dynamics (SINDy) used in LaSDI, the variance reduction of the weak form guarantees a robust and precise latent space recovery, hence allowing for a fast, robust, and accurate simulation. We demonstrate the efficacy of WLaSDI vs. LaSDI on several common benchmark examples including viscid and inviscid Burgers', radial advection, and heat conduction. For instance, in the case of 1D inviscid Burgers' simulations with the addition of up to 100% Gaussian white noise, the relative error remains consistently below 6% for WLaSDI, while it can exceed 10,000% for LaSDI. Similarly, for radial advection simulations, the relative errors stay below 15% for WLaSDI, in stark contrast to the potential errors of up to 10,000% with LaSDI. Moreover, speedups of several orders of magnitude can be obtained with WLaSDI. For example applying WLaSDI to 1D Burgers' yields a 140X speedup compared to the corresponding full order model.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

STAIR 2.0: A Generic and Automatic Algorithm to Fuse Modis, Landsat, and Sentinel-2 to Generate 10 m, Daily, and Cloud-/Gap-Free Surface Reflectance Product

Remote sensing datasets with both high spatial and high temporal resolution are critical for monitoring and modeling the dynamics of land surfaces. However, no current satellite sensor could simultaneously achieve both high spatial resolution and high revisiting frequency. Therefore, the integration of different sources of satellite data to produce a fusion product has become a popular solution to address this challenge. Many methods have been proposed to generate synthetic images with rich spatial details and high temporal frequency by combining two types of satellite datasets—usually frequent coarse-resolution images (e.g., MODIS) and sparse fine-resolution images (e.g., Landsat). In this paper, we introduce STAIR 2.0, a new fusion method that extends the previous STAIR fusion framework, to fuse three types of satellite datasets, including MODIS, Landsat, and Sentinel-2. In STAIR 2.0, input images are first processed to impute missing-value pixels that are due to clouds or sensor mechanical issues using a gap-filling algorithm. The multiple refined time series are then integrated stepwisely, from coarse- to fine- and high-resolution, ultimately providing a synthetic daily, high-resolution surface reflectance observations. We applied STAIR 2.0 to generate a 10-m, daily, cloud-/gap-free time series that covers the 2017 growing season of Saunders County, Nebraska. Moreover, the framework is generic and can be extended to integrate more types of satellite data sources, further improving the quality of the fusion product. View Full-Text

47 OTHER INSTRUMENTATION↗
DOE OSTI2017

Extreme-Scale Algorithms & Software Resilience (EASIR) Architecture-Aware Algorithms for Scalable Performance and Resilience on Heterogeneous Architectures

This project addresses both communication-avoiding algorithms, and reproducible floating-point computation. Communication, i.e. moving data, either between levels of memory or processors over a network, is much more expensive per operation than arithmetic (measured in time or energy), so we seek algorithms that greatly reduce communication. We developed many new algorithms for both dense and sparse, and both direct and iterative linear algebra, attaining new communication lower bounds, and getting large speedups in many cases. We also extended this work in several ways: (1) We minimize writes separately from reads, since writes may be much more expensive than reads on emerging memory technologies, like Flash, sometimes doing asymptotically fewer writes than reads. (2) We extend the lower bounds and optimal algorithms to arbitrary algorithms that may be expressed as perfectly nested loops accessing arrays, where the array subscripts may be arbitrary affine functions of the loop indices (eg A(i), B(i,j+k, k+3*m-7, …) etc.). (3) We extend our communication-avoiding approach to some machine learning algorithms, such as support vector machines. This work has won a number of awards. We also address reproducible floating-point computation. We define reproducibility to mean getting bitwise identical results from multiple runs of the same program, perhaps with different hardware resources or other changes that should ideally not change the answer. Many users depend on reproducibility for debugging or correctness. However, dynamic scheduling of parallel computing resources, combined with nonassociativity of floating point addition, makes attaining reproducibility a challenge even for simple operations like summing a vector of numbers, or more complicated operations like the Basic Linear Algebra Subprograms (BLAS). We describe an algorithm that computes a reproducible sum of floating point numbers, independent of the order of summation. The algorithm depends only on a subset of the IEEE Floating Point Standard 754-2008, uses just 6 words to represent a “reproducible accumulator,” and requires just one read-only pass over the data, or one reduction in parallel. New instructions based on this work are being considered for inclusion in the future IEEE 754-2018 floating-point standard, and new reproducible BLAS are being considered for the next version of the BLAS standard.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

On the Strong Convergence of Forward-Backward Splitting in Reconstructing Jointly Sparse Signals

We consider the problem of reconstructing an infinite set of sparse, finite-dimensional vectors, that share a common sparsity pattern, from incomplete measurements. This is in contrast to the work (Daubechies et al., Pure Appl. Math. 57(11), 1413–1457, 2004), where the single vector signal can be infinite-dimensional, and (Fornasier and Rauhut, SIAM J. Numer. Anal. 46(2), 577613, 2008), which extends the aforementioned work to the joint sparse recovery of finite number of infinite-dimensional vectors. In our case, to take account of the joint sparsity and promote the coupling of nonvanishing components, we employ a convex relaxation approach with mixed norm penalty ℓ 2,1 . This paper discusses the computation of the solutions of linear inverse problems with such relaxation by a forward-backward splitting algorithm. However, since the solution matrix possesses infinitely many columns, the arguments of Daubechies et al. (Pure Appl. Math. 57(11), 1413–1457, 2004) no longer apply. As such, we establish new strong convergence results for the algorithm, in particular when the set of jointly sparse vectors is infinite.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Automated identification of crystallographic ligands using sparse-density representations

A novel procedure for identifying ligands in macromolecular crystallographic electron-density maps is introduced. Density clusters in such maps can be rapidly attributed to one of 82 different ligands in an automated manner. A novel procedure for the automatic identification of ligands in macromolecular crystallographic electron-density maps is introduced. It is based on the sparse parameterization of density clusters and the matching of the pseudo-atomic grids thus created to conformationally variant ligands using mathematical descriptors of molecular shape, size and topology. In large-scale tests on experimental data derived from the Protein Data Bank, the procedure could quickly identify the deposited ligand within the top-ranked compounds from a database of candidates. This indicates the suitability of the method for the identification of binding entities in fragment-based drug screening and in model completion in macromolecular structure determination.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

A detection of the sea level fingerprint of Greenland Ice Sheet melt

Rapid melting of ice sheets and glaciers drives a unique geometry, or fingerprint, of sea level change. However, the detection of individual fingerprints has been challenging because of sparse observations at high latitudes and the difficulty of disentangling ocean dynamic variability from the signal. Here we predict the fingerprint of Greenland Ice Sheet (GrIS) melt using recent ice mass loss estimates from radar altimetry data and model reconstructions of nearby glaciers and compare this prediction to an independent, altimetry-derived sea surface height trend corrected for ocean dynamic variability in the region adjacent to the ice sheet. A statistically significant correlation between the two fields (P < 0.001) provides an unambiguous observational detection of the near-field sea level fingerprint of recent GrIS melting in our warming world.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Algorithms for X-Ray Imaging of Single Particles

Our group develops image reconstruction algorithms and applies them to data collected with x-ray sources (synchrotron, XFEL) and electron microscopes. Some of the challenges we face are (1) extremely low signal levels and noise, (2) missing information (phase, sample orientation), and (3) large, very sparse datasets. We have made significant progress in all three of these areas in our DOE project. This final report highlights two results produced during the extension of the project that proved to be spectacular successes. Publications and software products generated over the entire project period are listed at the end of the report.

97 MATHEMATICS AND COMPUTING↗