Future machine learning strategies for materials process optimization will likely replace human capital-intensive artisan research with autonomous and/or accelerated approaches. Such automation enables accelerated multimodal characterization that simultaneously minimizes human errors, lowers costs, enhances statistical sampling, and allows scientists to allocate their time to critical thinking instead of repetitive manual tasks. Previous acceleration efforts to synthesize and evaluate materials have often employed elaborate robotic self-driving laboratories or used specialized strategies that are difficult to generalize. Herein we describe an implemented workflow for accelerating the multimodal characterization of a combinatorial set of 915 electroplated Ni and Ni–Fe thin films resulting in a data cube with over 160,000 individual data files. Our acceleration strategies do not require manufacturing-scale resources and are thus amenable to typical materials research facilities in academic, government, or commercial laboratories. The workflow demonstrated the acceleration of six characterization modalities: optical microscopy, laser profilometry, X-ray diffraction, X-ray fluorescence, nanoindentation, and tribological (friction and wear) testing, each with speedup factors ranging from 13–46x. In addition, automated data upload to a repository using FAIR data principles was accelerated by 64x.
Hierarchical optimization modeling in an algebraic modeling environment facilitates construction of large models with many interchangeable sub-models. However, for dynamic simulation and optimization applications, a flattened structure that preserves time indexing is preferred. To convert from a structure that facilitates model construction to a structure that facilitates dynamic optimization, the concept of reshaping an optimization model is introduced along with the recently developed utilities in the Pyomo algebraic modeling environment that make this possible. The application of these utilities to model predictive control simulations and partial differential equation (PDE) discretization stability analysis is discussed, and two challenging nonlinear model predictive control case studies are presented to demonstrate the advantages of this approach.
Access to accurate solar resource data is critical for numerous applications, including estimating the yield of solar energy systems, developing radiation models, and validating irradiance datasets. However, lack of standardization in data formats and access interfaces across providers constitutes a major barrier to entry for new users. pvlib python's iotools subpackage aims to solve this issue by providing standardized Python functions for reading local files and retrieving data from external providers. All functions follow a uniform pattern and return convenient data outputs, allowing users to seamlessly switch between data providers and explore alternative datasets. The pvlib package is community-developed on GitHub: https://github.com/pvlib/pvlib-python. As of pvlib python version 0.9.5, the iotools subpackage supports 12 different datasets, including ground measurement, reanalysis, and satellite-derived irradiance data. The supported ground measurement networks include the Baseline Surface Radiation Network (BSRN), NREL MIDC, SRML, SOLRAD, SURFRAD, and the US Climate Reference Network (CRN). Additionally, satellite-derived and reanalysis irradiance data from the following sources are supported: PVGIS (SARAH & ERA5), NSRDB PSM3, and CAMS Radiation Service (including McClear clear-sky irradiance).
Recent developments integrating micromechanics and neural networks offer promising paths for rapid predictions of the response of heterogeneous materials with similar accuracy as direct numerical simulations. The deep material network is one such approaches, featuring a multi-layer network and micromechanics building blocks trained on anisotropic linear elastic properties. Once trained, the network acts as a reduced-order model, which can extrapolate the material’s behavior to more general constitutive laws, including nonlinear behaviors, without the need to be retrained. However, current training methods initialize network parameters randomly, incurring inevitable training and calibration errors. Here, we introduce a way to visualize the network parameters as an analogous unit cell and use this visualization to “quilt” patches of shallower networks to initialize deeper networks for a recursive training strategy. The result is an improvement in the accuracy and calibration performance of the network and an intuitive visual representation of the network for better explainability.
The properties of electrons in matter are of fundamental importance. They give rise to virtually all material properties and determine the physics at play in objects ranging from semiconductor devices to the interior of giant gas planets. Modeling and simulation of such diverse applications rely primarily on density functional theory (DFT), which has become the principal method for predicting the electronic structure of matter. While DFT calculations have proven to be very useful, their computational scaling limits them to small systems. We have developed a machine learning framework for predicting the electronic structure on any length scale. It shows up to three orders of magnitude speedup on systems where DFT is tractable and, more importantly, enables predictions on scales where DFT calculations are infeasible. Our work demonstrates how machine learning circumvents a long-standing computational bottleneck and advances materials science to frontiers intractable with any current solutions.
International Journal of Networked and Distributed Computing
Shrestha, Madhukar; Kim, Yonghyun; Oh, Jeehyun; Rhee, Junghwan (John); Choe, Yung R.; Zuo, Fei; Park, Myungah; Qian, Gang
System provenance forensic analysis has been studied by a large body of research work. This area needs fine granularity data such as system calls along with event fields to track the dependencies of events. While prior work on security datasets has been proposed, we found a useful dataset of realistic attacks and details that are needed for high-quality provenance tracking is lacking. We created a new dataset of eleven vulnerable cases for system forensic analysis. It includes the full details of system calls including syscall parameters. Realistic attack scenarios with real software vulnerabilities and exploits are used. For each case, we created two sets of benign and adversary scenarios which are manually labeled for supervised machine-learning analysis. In addition, we present an algorithm to improve the data quality in the system provenance forensic analysis. We demonstrate the details of the dataset events and dependency analysis of our dataset cases.
PyApprox is a Python-based one-stop-shop for probabilistic analysis of numerical models such as those used in the earth, environmental and engineering sciences. Easy to use and extendable tools are provided for constructing surrogates, sensitivity analysis, Bayesian inference, experimental design, and forward uncertainty quantification. The algorithms implemented represent a wide range of methods for model analysis developed over the past two decades, including recent advances in multi-fidelity approaches that use multiple model discretizations and/or simplified physics to significantly reduce the computational cost of various types of analyses. An extensive set of Benchmarks from the literature is also provided to facilitate the easy comparison of new or existing algorithms for a wide range of model analyses. This paper introduces PyApprox and its various features, and presents results demonstrating the utility of PyApprox on a benchmark problem modeling the advection of a tracer in groundwater.
We report on the substantial advancement of long wavelength InAs-based interband cascade lasers (ICLs) utilizing advanced waveguides formed from hybrid cladding layers and targeting the 10-12μm wavelength region. Modifications in the hole injector have improved carrier transport in these ICLs, resulting in significantly reduced threshold voltages (Vth) as low as 3.62 V at 80 K. Consequently, much higher voltage efficiencies were observed, peaking at about 73% at 10.3μm and allowing for large output powers of more than 100 mW/facet. Also, low threshold current densities (Jth) of 8.8 A/cm2 in cw mode and 7.6 A/cm2 in pulsed mode near 10μm were observed; a result of adjustments in the GaInSb hole well composition intended to reduce the overall strain accumulation in the ICL. Furthermore, an ICL from the second wafer operating at a longer wavelength achieved a peak voltage efficiency of 57% at 11.7μm, with a peak output power of more than 27 mW/facet. This ICL went on to lase beyond 12μm in both cw and pulsed modes, representing a new milestone in long wavelength coverage for ICLs with the standard W-QW active region.
The fraction of tritium converted to the water form in a fire scenario is one of the metrics of greatest interest for radiological safety assessments. The conversion fraction is one of the prime variables contributing to the hazard assessment. This paper presents measurements of oxidation rates for the non-radioactive hydrogen isotopes (protium and deuterium) at sub-flammable concentrations that are typical of many of the most likely tritium release scenarios. These measurements are fit to a simplified 1-step kinetic rate expression, and the isotopic trends for protium and deuterium are extrapolated to produce a model appropriate for tritium. The effects of the new kinetic models are evaluated via CFD simulations of an ISO-9705 standard room fire that includes a trace release of hydrogen isotope (tritium), illustrating the high importance of the correct (measurement-based) kinetics to the outcome of the simulated conversion.
R. toruloides is an oleaginous yeast, with diverse metabolic capacities and high tolerance for inhibitory compounds abundant in plant biomass hydrolysates. While R. toruloides grows on several pentose sugars and alcohols, further engineering of the native pathway is required for efficient conversion of biomass-derived sugars to higher value bioproducts. A previous high-throughput study inferred that R. toruloides possesses a non-canonical l-arabinose and d-xylose metabolism proceeding through d-arabitol and d-ribulose. In this study, we present a combination of genetic and metabolite data that refine and extend that model. Chiral separations definitively illustrate that d-arabitol is the enantiomer that accumulates under pentose metabolism. Deletion of putative d-arabitol-2-dehydrogenase (RTO4_9990) results in > 75% conversion of d-xylose to d-arabitol, and is growth-complemented on pentoses by heterologous xylulose kinase expression. Deletion of putative d-ribulose kinase (RTO4_14368) arrests all growth on any pentose tested. Analysis of several pentose dehydrogenase mutants elucidates a complex pathway with multiple enzymes mediating multiple different reactions in differing combinations, from which we also inferred a putative l-ribulose utilization pathway. Our results suggest that we have identified enzymes responsible for the majority of pathway flux, with additional unknown enzymes providing accessory activity at multiple steps. Further biochemical characterization of the enzymes described here will enable a more complete and quantitative understanding of R. toruloides pentose metabolism. These findings add to a growing understanding of the diversity and complexity of microbial pentose metabolism.
Computer vision models have great potential as tools for international nuclear safeguards verification activities, but off-the-shelf models require fine-tuning through transfer learning to detect relevant objects. Because open-source examples of safeguards-relevant objects are rare, and to evaluate the potential of synthetic training data for computer vision, we present the Limbo dataset. Limbo includes both real and computer-generated images of uranium hexafluoride containers for training computer vision models. We generated these images iteratively based on results from data validation experiments that are detailed here. The findings from these experiments are applicable both for the safeguards community and the broader community of computer vision research using synthetic data.
Behind-the-meter (BTM) battery energy storage systems (BESS) are undergoing rapid deployment. Simple equations to estimate the installed cost of BTM BESS are often necessary when a rigorous, bottom-up cost estimate is not available or not appropriate, in applications such as energy system modeling, informing a BESS sizing decision, and cost benchmarking. Drawing on project-level data from California, I estimate several predictive regression models of the installed cost of a BTM BESS as a function of energy capacity and power capacity. The models are evaluated for in-sample goodness-of-fit and out-of-sample predictive accuracy. The results of these analyses indicate stronger empirical support for models with natural log transformations of installed cost, energy, and power as compared against widely-used models that posit a linear relationship among the untransformed versions of these variables. Building on these results, I present a logarithmic model that can predict installed cost conditional on energy capacity, power capacity, AC or DC coupling with distributed generation, customer sector, and local wages for electricians. I document how the model can be easily extrapolated to future years, either with forecasts from other sources or by re-estimating the parameters with the latest data.
This report summarizes the water inputs associated with four technologies playing diverse roles in energy transitions: hydrogen, solar photovoltaics (PV), wind, and batteries. Information in this report is drawn from multiple sources, including peer-reviewed literature, industry and international agency reports, EcoInvent life cycle inventory database, and subject matter expert (SME) consultations. Where possible, insights that characterized water requirements for specific stages of the technology development (e.g., operations, manufacturing, and mining) were prioritized over broader cradle-to-gate assessment values. Furthermore, both direct and indirect water requirements (i.e., associated with associated energy inputs) were considered in this literature review.
The 2-year Puerto Rico Grid Resilience and Transition to 100% Renewable Energy Study analyzed stakeholder-driven pathways to Puerto Rico’s clean energy future. Outputs relating to electricity demand modeling were partially informed by estimates of electric vehicle adoption across all classes of medium- and heavy-duty vehicles (MHDVs), and the ensuing charging loads. To create these estimates, the team developed a transportation model for MHDVs in Puerto Rico to estimate the amount and geospatial distribution of energy used. Charging schedules for the different end uses of MHDVs were then used to construct electric load shapes assuming a portion of those vehicles would be replaced by battery electric counterparts. Study results showed that, by 2050, electric vehicles may constitute roughly 50% of the MHDV population in Puerto Rico. The resulting electrical demand curve attributable to MHDV charging showed that, for solar energy-based electrical systems with limited energy storage, this demand may create challenges unless appropriately managed either on the demand or supply side.
The subject of Task F of DECOVALEX-2023 concerns performance assessment modelling of radioactive waste disposal in deep mined repositories. The primary objectives of Task F are to build confidence in the models, methods, and software used for performance assessment (PA) of deep geologic nuclear waste repositories, and/or to bring to the fore additional research and development needed to improve PA methodologies. In Task F2-(salt), these objectives have been accomplished through staged development and comparison of the models and methods used by participating teams in their PA frameworks. Coupled-process submodels and deterministic simulations of the entire PA model for a reference scenario for waste disposal in domal salt have been conducted. The task specification has been updated continuously since the initiation of the project to reflect the staged development of the conceptual repository model and performance metrics.
Redox flow batteries (RFBs) that incorporate solid energy-storing materials are attractive for high-capacity grid-scale energy storage due to their markedly higher theoretical energy densities compared to their fully liquid counterparts. However, this promise of higher energy density comes at the expense of rate capability. In this work we exploit a ZnO nanorod-decorated Ni foam scaffold to create a high surface area Li metal anode capable of rates up to 10 mA cm−2, a 10× improvement over traditional planar designs. The ZnO nanorods enhance Li metal wettability and promote uniform Li nucleation, allowing the RFB to be initially operated with a prelithiated (charged) anode, or with a safety-conscious, Li-less, fully discharged anode. 5 mgS cm−1 were cycled using a mediated S cathode, whereby redox mediators help oxidize and reduce solid S particles. At 2.4 mgS cm−2 and 10 mA cm−2, the RFB becomes limited by the mediation of solid S. Nevertheless, a respectable energy density of 20.3 Wh L−1 is demonstrated, allowing considerable increase if the S mediation rate can be further improved. Lessons learned here may be broadly applied to RFBs with alkali metal anodes, offering an avenue for safe, dense, grid-scale energy storage.
There is currently very limited research into how experts analyze and assess potentially fraudulent content in their expertise areas, and most research within the disinformation space involves very limited text samples (e.g., news headlines). The overarching goal of the present study was to explore how an individual’s psychological profile and the linguistic features in text might influence an expert’s ability to discern disinformation/fraudulent content in academic journal articles. At a high level, the current design tasked experts with reading journal articles from their area of expertise and indicating if they thought an article was deceptive or not. Half the articles they read were journal papers that had been retracted due to academic fraud. Demographic and psychological inventory data collected on the participants was combined with performance data to generate insights about individual expert susceptibility to deception. Our data show that our population of experts were unable to reliably detect deception in formal technical writing. Several psychological dimensions such as comfort with uncertainty and intellectual humility may provide some protection against deception. This work informs our understanding of expert susceptibility to potentially fraudulent content within official, technical information and can be used to inform future mitigative efforts and provide a building block for future disinformation work.
Achieving robust and efficient drilling is a critical part of reducing the cost of geothermal energy exploration and extraction. Drilling performance is often evaluated using one or more of three key metrics: depth of cut (DOC), rate of penetration (ROP), and mechanical specific energy (MSE). All three of these quantities are related to each other. DOC refers to the depth a bit penetrates into rock during drilling. This is an important quantity for estimating bit behavior. ROP is the simply the DOC multiplied by the rotational rate, and represents how quickly the drill bit is advancing through the ground. ROP is often the parameter used for drilling control and optimization. Finally, MSE provides insight into drilling efficiency and rock type. MSE calculations rely on ROP, drilling force, and drilling torque. Surface-based sensors at the top of the drill are often used to measure all these quantities. However, top-hole measurements can deviate substantially from the behavior at the bit due to lag, vibrations, and friction. Therefore, relying only on top-hole information can lead to suboptimal drilling control. In this work, we describe recent progress towards estimating ROP, DOC, and MSE using down-hole sensing. We assume down-hole measurements of torque, weight-on-bit (WOB). Our hypothesis is that these measurements can provide more rapid and accurate measures of drilling performance. We show how a multi-layer perceptron (MLP) machine learning algorithm can provide rapid and accurate performance when evaluated on experimental data taken from Sandia’s Hard Rock Drilling Facility. In addition, we implement our algorithms on an embedded system intended to emulate a bottom-hole-assembly for sensing and estimation. Our experimental results show that DOC can be estimated accurately and in real-time. These estimates when combined with measurements for rotary speed, torque, and force can provide improved estimates for ROP and MSE. These results have the potential to enable better drilling assessment, improved control, and extended component lifetimes.
This technical report serves to summarize a literature search conducted that covered confidence calibration. This report is meant to serve as a solid starting reference for individuals interested in learning more about the confidence calibration domain as well as for individuals more familiar with this work – as a summarizing document for calibration metrics is notably lacking in the literature. This report is not meant to serve as a comprehensive review of everything that has been done in this field – in fact, the reader is encouraged to look further into this domain. We describe confidence and calibration and discuss properties of good calibration metrics. We detail various calibration and calibration-tangential metrics, presenting equations, algorithms, parameters, and an analysis of strengths and weaknesses. We apply a subset of these metrics to eight proxy confidence assessment datasets. We examine the various metrics in the context of model confidence. Finally, we discuss promising future directions and outstanding questions.
