Results for “Randomized methods” — Page 53

DOE OSTI2020

Ring-opening metathesis polymerization of cobaltocenium derivative to prepare anion exchange membrane with high ionic conductivity

Abstract An efficient method to incorporate a cobaltocenium cation into polymers for anion exchange membranes is described. A cobaltocenium-containing norbornene derivative has been synthesized. It can be homopolymerized, or copolymerized with norbornene, by ring-opening metathesis polymerization (ROMP) with quantitative yields. The composition of the random copolymers can be precisely controlled by adjusting the feed ratio of the monomers. Additionally, the resulting polymers can be cast into thin robust membranes. The morphology of the membranes is characterized by humidity controlled SAXS and TEM, showing disordered interconnected cobaltocenium domains. The high ionic conductivity suggests that such membranes are good candidates for anion exchange membranes.

42 ENGINEERING↗

DOE OSTI2021

Quantum circuit cutting with maximum-likelihood tomography

Abstract We introduce maximum-likelihood fragment tomography (MLFT) as an improved circuit cutting technique for running clustered quantum circuits on quantum devices with a limited number of qubits. In addition to minimizing the classical computing overhead of circuit cutting methods, MLFT finds the most likely probability distribution for the output of a quantum circuit, given the measurement data obtained from the circuit’s fragments. We demonstrate the benefits of MLFT for accurately estimating the output of a fragmented quantum circuit with numerical experiments on random unitary circuits. Finally, we show that circuit cutting can estimate the output of a clustered circuit with higher fidelity than full circuit execution, thereby motivating the use of circuit cutting as a standard tool for running clustered circuits on quantum hardware.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

High pressure chemical reactivity and structural study of the Na–P and Li–P systems

In this work, the Na–P and Li–P chemical systems were studied under pressure using synchrotron X-ray diffraction in a diamond anvil cell up to 20 GPa, combined with the AIRSS ab initio random structure searching technique. The results reveal an enhanced reactivity of both alkali metals with phosphorous at slightly elevated pressures. This enables the synthesis of Li 3 P and Na 3 P at room temperature (RT) starting from element precursors, bypassing the established chemical synthesis methods. Both compounds undergo a pressure-induced phase transition from the hexagonal Na 3 As-type structure (stable at ambient conditions) towards a Fm m (FCC) structure that remains stable up to 20 GPa. Attempts to synthesize compounds with higher alkali metal content (such as Li 5 P) using high-temperature and -pressure conditions (up to 2000+ K and 30 GPa), inspired by recent theoretical predictions, were not successful.

25 ENERGY STORAGE↗

DOE OSTI2020

How accurate is a machine learning-based wind speed extrapolation under a round-robin approach?

As the size of commercial wind turbines keeps increasing, having accurate ways to vertically extrapolate wind speed is essential to obtain a precise characterization of the wind resource for wind energy production. Recently, machine learning has been proposed and applied to extrapolate wind speed to hub heights. However, previous studies trained and tested the machine learning methods at the same site, giving them an unfair advantage over the conventional extrapolation techniques, which are instead more universal. Here, we use data from four sites in Oklahoma to test a round-robin validation approach for machine learning, under which we train a random forest at a site, and test it at a different site, where the model has no prior knowledge of the wind resource. We quantify how the accuracy of this technique varies with distance from the training site, and we find that it outperforms conventional techniques for wind extrapolation at all the considered spatial separations. We then assess how the accuracy of the machine-learning based approach varies when it is used to predict wind speed in a wind farm far wake. Finally, we explore as case study the performance of the random forest in extrapolating winds during a low-level jet event.

17 WIND ENERGY↗

DOE OSTI2024

Structure Factors for Hot Neutron Matter from Ab Initio Lattice Simulations with High-Fidelity Chiral Interactions

We present the first ab initio lattice calculations of spin and density correlations in hot neutron matter using high-fidelity interactions at next-to-next-to-next-to-leading order in chiral effective field theory. These correlations have a large impact on neutrino heating and shock revival in core-collapse supernovae and are encapsulated in functions called structure factors. Unfortunately, calculations of structure factors using high-fidelity chiral interactions were well out of reach using existing computational methods. In this Letter, we solve the problem using a computational approach called the rank-one operator (RO) method. The RO method is a general technique with broad applications to simulations of fermionic many-body systems. It solves the problem of exponential scaling of computational effort when using perturbation theory for higher-body operators and higher-order corrections. Using the RO method, we compute the vector and axial static structure factors for hot neutron matter as a function of temperature and density. Here, the ab initio lattice results are in good agreement with virial expansion calculations at low densities but are more reliable at higher densities. Random phase approximation codes used to estimate neutrino opacity in core-collapse supernovae simulations can now be calibrated with ab initio lattice calculations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2020

Replicating Machine Learning Experiments in Materials Science

Transparency and reproducibility are important aspects of validation for Machine Learning (ML) models that are not fully understood and applies independently of the application domain.We offer a case study of reproducibility that highlights the challenges encountered when attempting to reproduce analyzes obtained with Machine Learning methods in materials informatics. Our study explores prediction results obtained with ML models and issues in training data serving as input. We discuss challenges related to theory-driven and numerical errors in training data, lack of reproducibility across platforms and versions, and effects of randomness when varying hyperparameters. In addition to model accuracy, a main metric of interest in the ML community, our results show that model sensitivity may be equally important for applying ML in domain applications such a materials science.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Construction of a Compact Array of Microplasma Jet Devices and Its Application for Random Mutagenesis of Rhodosporidium toruloides

A small and efficient DNA mutation-inducing machine was constructed with an array of microplasma jet devices (7 × 1) that can be operated at atmospheric pressure for microbial mutagenesis. Using this machine, we report disruption of a plasmid DNA and generation of mutants of an oleaginous yeast Rhodosporidium toruloides. Specifically, a compact-sized microplasma channel (25 × 20 × 2 mm 3 ) capable of generating an electron density of greater than 10 13 cm –3 was constructed to produce reactive species (N 2 *, N 2 +, O, OH, and H α ) under helium atmospheric conditions to induce DNA mutagenesis. The length of microplasma channels in the device played a critical role in augmenting both the volume of plasma and the concentration of reactive species. First, we confirmed that microplasma treatment can linearize a plasmid by creating nicks in vitro. Second, we treated R. toruloides cells with a jet device containing 7 microchannels for 5 min; 94.8% of the treated cells were killed, and 0.44% of surviving cells showed different colony colors as compared to their parental colony. Microplasma-based DNA mutation is energy-efficient and can be a safe alternative for inducing mutations compared to conventional methods using toxic mutagens. In conclusion, this compact and scalable device is amenable for industrial strain improvement involving large-scale mutagenesis.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Realization of Non‐Equilibrium Wurtzite Structure in Heterovalent Ternary MgSiN 2 Film Grown by Reactive Sputtering

The piezoelectric and ferroelectric applications of heterovalent ternary materials are not well explored. Epitaxial MgSiN 2 films are grown at 600 °C on (111)Pt//(001)Al 2 O 3 substrates by the reactive sputtering method using metallic Mg and Si under the N 2 atmosphere. Detailed X-ray diffraction measurements and transmission electron microscopy observations revealed that the epitaxially grown films on the substrates have a hexagonal wurtzite structure with c-axis out-of-plane orientation. The random occupation of this structure by Mg and Si differs from that of the previously reported structure in which these two cations periodically occupy the cationic sites. However, the lattice spacings closely approximate those that are previously reported, irrespective of the ordering, and they are almost comparable with those of (Al 0.8 Sc 0.2 )N. The wide bandgap of >5.0 eV in deposited MgSiN 2 is compatible with that of AlN and suggests durability against the application of strong external electric fields, possibly to induce polarization switching. In addition, MgSiN 2 is shown to have piezoelectric properties with an effective d 33 value of 2.3 pm V −1 for the first time. This work demonstrates the compositional expansion of hexagonal wurtzite to heterovalent ternary nitrides for novel piezoelectric materials, whose ferroelectricity is expected.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Assessing Machine Learning as a Tool to Explain Variance in Deployed Photovoltaic (PV) System Degradation

Degradation remains a large uncertainty in forecasting production for PV plants, creating significant risk for developers and financiers. This study aims to quantify the distribution and drivers of degradation across 10,000 PV systems deployed for distributed or utility generation by training a machine learning model to predict year-over-year degradation rates from metadata characteristics. A combination of K-Means clustering and random forest regressor were found to associate multiple metadata features as potential drivers of degradation, including module characteristics, system design, and climate features. From this, it is inferred that if machine learning is able to find complex patterns between metadata features and system performance loss, such methods can be employed to help developers and financiers make data-informed decisions when estimating long-term energy production forecasts in financial models.

Dunn, Jimmy C.↗

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 OSTI2022

Electrodes, lithium-ion batteries, and methods of making and using same

Described herein are improved composite anodes and lithium-ion batteries made therefrom. Further described are methods of making and using the improved anodes and batteries. In general, the anodes include a porous composite having a plurality of agglomerated nanocomposites. At least one of the plurality of agglomerated nanocomposites is formed from a dendritic particle, which is a three-dimensional, randomly-ordered assembly of nanoparticles of an electrically conducting material and a plurality of discrete non-porous nanoparticles of a non-carbon Group 4A element or mixture thereof disposed on a surface of the dendritic particle. At least one nanocomposite of the plurality of agglomerated nanocomposites has at least a portion of its dendritic particle in electrical communication with at least a portion of a dendritic particle of an adjacent nanocomposite in the plurality of agglomerated nanocomposites.

Yushin, Gleb↗

DOE OSTI2022

Electrodes, lithium-ion batteries, and methods of making and using same

Described herein are improved composite anodes and lithium-ion batteries made therefrom. Further described are methods of making and using the improved anodes and batteries. In general, the anodes include a porous composite having a plurality of agglomerated nanocomposites. At least one of the plurality of agglomerated nanocomposites is formed from a dendritic particle, which is a three-dimensional, randomly-ordered assembly of nanoparticles of an electrically conducting material and a plurality of discrete non-porous nanoparticles of a non-carbon Group 4A element or mixture thereof disposed on a surface of the dendritic particle. At least one nanocomposite of the plurality of agglomerated nanocomposites has at least a portion of its dendritic particle in electrical communication with at least a portion of a dendritic particle of an adjacent nanocomposite in the plurality of agglomerated nanocomposites.

Yushin, Gleb↗

DOE OSTI2023

Electrodes, lithium-ion batteries, and methods of making and using same

Described herein are improved composite anodes and lithium-ion batteries made therefrom. Further described are methods of making and using the improved anodes and batteries. In general, the anodes include a porous composite having a plurality of agglomerated nanocomposites. At least one of the plurality of agglomerated nanocomposites is formed from a dendritic particle, which is a three-dimensional, randomly-ordered assembly of nanoparticles of an electrically conducting material and a plurality of discrete non-porous nanoparticles of a non-carbon Group 4A element or mixture thereof disposed on a surface of the dendritic particle. At least one nanocomposite of the plurality of agglomerated nanocomposites has at least a portion of its dendritic particle in electrical communication with at least a portion of a dendritic particle of an adjacent nanocomposite in the plurality of agglomerated nanocomposites.

Yushin, Gleb↗

DOE OSTI2023

Electrodes, lithium-ion batteries, and methods of making and using same

Described herein are improved composite anodes and lithium-ion batteries made therefrom. Further described are methods of making and using the improved anodes and batteries. In general, the anodes include a porous composite having a plurality of agglomerated nanocomposites. At least one of the plurality of agglomerated nanocomposites is formed from a dendritic particle, which is a three-dimensional, randomly-ordered assembly of nanoparticles of an electrically conducting material and a plurality of discrete non-porous nanoparticles of a non-carbon Group 4A element or mixture thereof disposed on a surface of the dendritic particle. At least one nanocomposite of the plurality of agglomerated nanocomposites has at least a portion of its dendritic particle in electrical communication with at least a portion of a dendritic particle of an adjacent nanocomposite in the plurality of agglomerated nanocomposites.

Yushin, Gleb↗

DOE OSTI2025

Evaluation of Drilling Performance at The Geysers with Machine Learning Methods Using Geologic Data

A recent well, GDC-36, was drilled in The Geysers Geothermal Field served in a Department of Energy-industry to demonstrate improved drilling performance with polycrystalline diamond compact (PDC) bits. Both PDC and roller cone drill bits were used to drill this well. Key challenges encountered during drilling included lost circulation in the mud-drilled section, and bit damage interfacial severity in the deeper, air-drilled section. The objective of this study is to evaluate the drilling performance in relation to the local geological characteristics using machine learning methods. By applying K-clustering to the sonic log data, we were able to identify areas correlated with measured lost circulation. Also, the boundaries defined by clustering of the mineralogical and lithological data from the mud logs correlate well with interfacial severity during drilling. A random forest model was employed to build correlation between drilling data and rock strength. The confined compressive strength (CCS) of the rock in the training of the machine learning model was inferred from the dipole sonic log. The R-squared of the testing data is 0.78, and the RMSE (Root Mean Squared Error) is 0.06. The trained model was used to forecast rock strength for the section where sonic log data are not available. CCS could also be inferred from mud logs provided the relationship between mineralogy and rock strength is established through core testing data.

15 GEOTHERMAL ENERGY↗

DOE OSTI2022

Substructure in the stellar halo near the Sun: I. Data-driven clustering in integrals-of-motion space

Context. Merger debris is expected to populate the stellar haloes of galaxies. In the case of the Milky Way, this debris should be apparent as clumps in a space defined by the orbital integrals of motion of the stars. Aims. Our aim is to develop a data-driven and statistics-based method for finding these clumps in integrals-of-motion space for nearby halo stars and to evaluate their significance robustly. Methods. We used data from Gaia EDR3, extended with radial velocities from ground-based spectroscopic surveys, to construct a sample of halo stars within 2.5 kpc from the Sun. We applied a hierarchical clustering method that makes exhaustive use of the single linkage algorithm in three-dimensional space defined by the commonly used integrals of motion energy E, together with two components of the angular momentum, L z and L ⊥ . To evaluate the statistical significance of the clusters, we compared the density within an ellipsoidal region centred on the cluster to that of random sets with similar global dynamical properties. By selecting the signal at the location of their maximum statistical significance in the hierarchical tree, we extracted a set of significant unique clusters. By describing these clusters with ellipsoids, we estimated the proximity of a star to the cluster centre using the Mahalanobis distance. Additionally, we applied the HDBSCAN clustering algorithm in velocity space to each cluster to extract subgroups representing debris with different orbital phases. Results. Our procedure identifies 67 highly significant clusters (> 3σ), containing 12% of the sources in our halo set, and 232 subgroups or individual streams in velocity space. In total, 13.8% of the stars in our data set can be confidently associated with a significant cluster based on their Mahalanobis distance. Inspection of the hierarchical tree describing our data set reveals a complex web of relations between the significant clusters, suggesting that they can be tentatively grouped into at least six main large structures, many of which can be associated with previously identified halo substructures, and a number of independent substructures. This preliminary conclusion is further explored in a companion paper, in which we also characterise the substructures in terms of their stellar populations. Conclusions. Our method allows us to systematically detect kinematic substructures in the Galactic stellar halo with a data-driven and interpretable algorithm. The list of the clusters and the associated star catalogue are provided in two tables available at the CDS.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

Fast GPU-Based Generation of Large Graph Networks From Degree Distributions

Synthetically generated, large graph networks serve as useful proxies to real-world networks for many graph-based applications. The ability to generate such networks helps overcome several limitations of real-world networks regarding their number, availability, and access. Here, we present the design, implementation, and performance study of a novel network generator that can produce very large graph networks conforming to any desired degree distribution. The generator is designed and implemented for efficient execution on modern graphics processing units (GPUs). Given an array of desired vertex degrees and number of vertices for each desired degree, our algorithm generates the edges of a random graph that satisfies the input degree distribution. Multiple runtime variants are implemented and tested: 1) a uniform static work assignment using a fixed thread launch scheme, 2) a load-balanced static work assignment also with fixed thread launch but with cost-aware task-to-thread mapping, and 3) a dynamic scheme with multiple GPU kernels asynchronously launched from the CPU. The generation is tested on a range of popular networks such as Twitter and Facebook, representing different scales and skews in degree distributions. Results show that, using our algorithm on a single modern GPU (NVIDIA Volta V100), it is possible to generate large-scale graph networks at rates exceeding 50 billion edges per second for a 69 billion-edge network. GPU profiling confirms high utilization and low branching divergence of our implementation from small to large network sizes. For networks with scattered distributions, we provide a coarsening method that further increases the GPU-based generation speed by up to a factor of 4 on tested input networks with over 45 billion edges.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Particle transport in Markov stochastic mixtures with spatial gradients

Markov geometries are a prototype class of stochastic media that are widely used to model complex disordered systems. In a series of recent works, we have shown that spatially homogeneous (but possibly non-isotropic) Poisson tessellations can be conveniently used in order to generate three-dimensional realizations of Markov media and thus characterize the features of particle transport e.g. in fragmented fuel elements following severe nuclear accidents or Rayleigh-Taylor turbulent mixing for inertial confinement fusion. In this paper we show that Poisson tessellations can be extended to take into account the presence of spatial gradients, which commonly occur in real-world applications due to material stratification. For this purpose, we will provide an explicit construction of spatially-non-homogeneous Poisson tessellations, fully characterize their statistical properties, and finally illustrate the behaviour of particle transport in such random media. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗