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

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Local and global decision fusion for cyber-physical system abnormality detection

Monitoring nodes may generate a series of current monitoring node values over time representing current operation of a cyber-physical system. A decision fusion computer platform may receive, from a local status determination module, an indication of whether each node has an initial local status of “normal”/“abnormal” and a local certainty score (with higher values of the local certainty score representing greater likelihood of abnormality). The computer platform may also receive, from a global status determination module, an indication of whether the system has an initial global status of “normal”/“abnormal” and a global certainty score. The computer platform may output, for each node, a fused local status of “normal” or “abnormal,” at least one fused local status being based on the initial global status. The decision fusion computer platform may also output a fused global status of “normal” or “abnormal” based on at least one initial local status.

Abbaszadeh, Masoud↗
DOE OSTI2020

Supercomputer-Based Ensemble Docking Drug Discovery Pipeline with Application to Covid-19

In this work, we present a supercomputer-driven pipeline for in silico drug discovery using enhanced sampling molecular dynamics (MD) and ensemble docking. Ensemble docking makes use of MD results by docking compound databases into representative protein binding-site conformations, thus taking into account the dynamic properties of the binding sites. We also describe preliminary results obtained for 24 systems involving eight proteins of the proteome of SARS-CoV-2. The MD involves temperature replica exchange enhanced sampling, making use of massively parallel supercomputing to quickly sample the configurational space of protein drug targets. Using the Summit supercomputer at the Oak Ridge National Laboratory, more than 1 ms of enhanced sampling MD can be generated per day. We have ensemble docked repurposing databases to 10 configurations of each of the 24 SARS-CoV-2 systems using AutoDock Vina. Comparison to experiment demonstrates remarkably high hit rates for the top scoring tranches of compounds identified by our ensemble approach. We also demonstrate that, using Autodock-GPU on Summit, it is possible to perform exhaustive docking of one billion compounds in under 24 h. Finally, we discuss preliminary results and planned improvements to the pipeline, including the use of quantum mechanical (QM), machine learning, and artificial intelligence (AI) methods to cluster MD trajectories and rescore docking poses.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Finding Strong Gravitational Lenses in the DESI DECam Legacy Survey

We perform a semi-automated search for strong gravitational lensing systems in the 9000 deg 2 Dark Energy Camera Legacy Survey (DECaLS), part of the Dark Energy Spectroscopic Instrument Legacy Imaging Surveys. The combination of the depth and breadth of these surveys are unparalleled at this time, making them particularly suitable for discovering new strong gravitational lensing systems. We adopt the deep residual neural network architecture developed by Lanusse et al. for the purpose of finding strong lenses in photometric surveys. We compile a training sample that consists of known lensing systems in the Legacy Surveys and the Dark Energy Survey as well as non-lenses in the footprint of DECaLS. In this paper we show the results of applying our trained neural network to the cutout images centered on galaxies typed as ellipticals in DECaLS. The images that receive the highest scores (probabilities) are visually inspected and ranked. Here we present 335 candidate strong lensing systems, identified for the first time.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

MULTICOM2 open-source protein structure prediction system powered by deep learning and distance prediction

Protein structure prediction is an important problem in bioinformatics and has been studied for decades. However, there are still few open-source comprehensive protein structure prediction packages publicly available in the field. In this paper, we present our latest open-source protein tertiary structure prediction system—MULTICOM2, an integration of template-based modeling (TBM) and template-free modeling (FM) methods. The template-based modeling uses sequence alignment tools with deep multiple sequence alignments to search for structural templates, which are much faster and more accurate than MULTICOM1. The template-free (ab initio or de novo) modeling uses the inter-residue distances predicted by DeepDist to reconstruct tertiary structure models without using any known structure as template. In the blind CASP14 experiment, the average TM-score of the models predicted by our server predictor based on the MULTICOM2 system is 0.720 for 58 TBM (regular) domains and 0.514 for 38 FM and FM/TBM (hard) domains, indicating that MULTICOM2 is capable of predicting good tertiary structures across the board. It can predict the correct fold for 76 CASP14 domains (95% regular domains and 55% hard domains) if only one prediction is made for a domain. The success rate is increased to 3% for both regular and hard domains if five predictions are made per domain. Moreover, the prediction accuracy of the pure template-free structure modeling method on both TBM and FM targets is very close to the combination of template-based and template-free modeling methods. This demonstrates that the distance-based template-free modeling method powered by deep learning can largely replace the traditional template-based modeling method even on TBM targets that TBM methods used to dominate and therefore provides a uniform structure modeling approach to any protein. Finally, on the 38 CASP14 FM and FM/TBM hard domains, MULTICOM2 server predictors (MULTICOM-HYBRID, MULTICOM-DEEP, MULTICOM-DIST) were ranked among the top 20 automated server predictors in the CASP14 experiment. After combining multiple predictors from the same research group as one entry, MULTICOM-HYBRID was ranked no. 5. The source code of MULTICOM2 is freely available at https://github.com/multicom-toolbox/multicom/tree/multicom_v2.0 .

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Disentangling Alzheimer’s disease neurodegeneration from typical brain ageing using machine learning

Abstract Neuroimaging biomarkers that distinguish between changes due to typical brain ageing and Alzheimer’s disease are valuable for determining how much each contributes to cognitive decline. Supervised machine learning models can derive multivariate patterns of brain change related to the two processes, including the Spatial Patterns of Atrophy for Recognition of Alzheimer’s Disease (SPARE-AD) and of Brain Aging (SPARE-BA) scores investigated herein. However, the substantial overlap between brain regions affected in the two processes confounds measuring them independently. We present a methodology, and associated results, towards disentangling the two. T1-weighted MRI scans of 4054 participants (48–95 years) with Alzheimer’s disease, mild cognitive impairment (MCI), or cognitively normal (CN) diagnoses from the Imaging-based coordinate SysTem for AGIng and NeurodeGenerative diseases (iSTAGING) consortium were analysed. Multiple sets of SPARE scores were investigated, in order to probe imaging signatures of certain clinically or molecularly defined sub-cohorts. First, a subset of clinical Alzheimer’s disease patients (n = 718) and age- and sex-matched CN adults (n = 718) were selected based purely on clinical diagnoses to train SPARE-BA1 (regression of age using CN individuals) and SPARE-AD1 (classification of CN versus Alzheimer’s disease) models. Second, analogous groups were selected based on clinical and molecular markers to train SPARE-BA2 and SPARE-AD2 models: amyloid-positive Alzheimer’s disease continuum group (n = 718; consisting of amyloid-positive Alzheimer’s disease, amyloid-positive MCI, amyloid- and tau-positive CN individuals) and amyloid-negative CN group (n = 718). Finally, the combined group of the Alzheimer’s disease continuum and amyloid-negative CN individuals was used to train SPARE-BA3 model, with the intention to estimate brain age regardless of Alzheimer’s disease-related brain changes. The disentangled SPARE models, SPARE-AD2 and SPARE-BA3, derived brain patterns that were more specific to the two types of brain changes. The correlation between the SPARE-BA Gap (SPARE-BA minus chronological age) and SPARE-AD was significantly reduced after the decoupling (r = 0.56–0.06). The correlation of disentangled SPARE-AD was non-inferior to amyloid- and tau-related measurements and to the number of APOE ε4 alleles but was lower to Alzheimer’s disease-related psychometric test scores, suggesting the contribution of advanced brain ageing to the latter. The disentangled SPARE-BA was consistently less correlated with Alzheimer’s disease-related clinical, molecular and genetic variables. By employing conservative molecular diagnoses and introducing Alzheimer’s disease continuum cases to the SPARE-BA model training, we achieved more dissociable neuroanatomical biomarkers of typical brain ageing and Alzheimer’s disease.

Hwang, Gyujoon↗
DOE OSTI2023

Robotics Plan in Support of DOME Testbed Operations

Various robotic tooling options have been evaluated for the NRIC DOME concept of operations (ConOps). Framatome was contracted to develop a wide-ranging list of commercial off the shelf (COTS) and custom robotic systems to be considered for performing the DOME ConOps functions. Subsequent project tasks from Framatome narrowed down the list and scored the most viable options. The abbreviated list, and associated scoring, has been reviewed and assessed to provide formal recommendations for the robotic ConOps functions of DOME. The overhead telescoping mast & Kraft arm assembly is recommended as the primary system for reactor demobilization and removal. The estimated cost is $\$700$K with a timeline to develop and deliver of about 2.5 years. The mast & Kraft arm would still require an overhead lift system for mobilization and installation. Either the refurbished polar crane or a new gantry crane delivery platform could both serve as the overhead lift and delivery system. The estimated costs for the refurbishment polar crane and new gantry crane systems are, respectively, $\$4.4$M and $\$3$M with about 2.5 years to develop and deliver. A Brokk + Kraft crawler & arm is recommended as a secondary robotic system to support the overhead mast & Kraft arm system. The Brokk + Kraft crawler & arm would cost an estimated $\$726$k and would take about 1.5 years to develop and deliver. The Boston Dynamics SPOT robot is also recommended for the in-service monitoring during experimentation operations. The estimated cost is $\$220$K with a timeline to deliver of 8-14 months. The Elios 3 aerial drone is recommended to perform large area radiation dose rate mapping and visual inspections. The estimated cost is a $\$100$K and will also need about 8-14 months to develop and deliver. A mockup rig is also recommended to be procured an employed to test & verify the robotic capabilities as well as provide needed training for operations personnel. It is estimated that a mockup rig would cost about $\$500$k and would take up to a year to develop and build.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

A Corrected Score Function Framework for Modelling Circadian Gene Expression

Many biological processes display oscillatory behaviour based on an approximately 24 h internal timing system specific to each individual. One process of particular interest is gene expression, for which several circadian transcriptomic studies have identified associations between gene expression during a 24 h period and an individual's health. A challenge with analysing data from these studies is that each individual's internal timing system is offset relative to the 24 h day-night cycle, where day–night cycle time is recorded for each collected sample. Laboratory procedures can accurately determine each individual's offset and determine the internal time of sample collection. However, these laboratory procedures are labour-intensive and expensive. Here, in this paper, we propose a corrected score function framework to obtain a regression model of gene expression given internal time when the offset of each individual is too burdensome to determine. A feature of this framework is that it does not require the probability distribution generating offsets to be symmetric with a mean of zero. Simulation studies validate the use of this corrected score function framework for cosinor regression, which is prevalent in circadian transcriptomic studies. Illustrations with data from three circadian transcriptomic studies further demonstrate that the proposed framework consistently mitigates bias relative to using a score function that does not account for this offset.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

A score-based diffusion model approach for adaptive learning of stochastic partial differential equation solutions

In this paper, we propose a novel framework for adaptively learning the time-evolving solutions of stochastic partial differential equations (SPDEs) using score-based diffusion models within a recursive Bayesian inference setting. SPDEs play a central role in modeling complex physical systems under uncertainty, but their numerical solutions often suffer from model errors and reduced accuracy due to incomplete physical knowledge and environmental variability. To address these challenges, we encode the governing physics into the score function of a diffusion model using simulation data and incorporate observational information via a likelihood-based correction in a reverse-time stochastic differential equation. This enables adaptive learning through iterative refinement of the solution as new data becomes available. To improve computational efficiency in high-dimensional settings, we introduce the ensemble score filter, a training-free approximation of the score function designed for real-time inference. Numerical experiments on benchmark SPDEs demonstrate the accuracy and robustness of the proposed method under sparse and noisy observations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Optimal band selection for target detection with a LWIR multispectral imager

Multispectral imaging can offer many benefits in cost, complexity, resolution, size, weight, and power, relative to hyperspectral imaging. When designing a multispectral system, spectral bandpasses can be selected using optimization algorithms configured to maximally separate target detection scores between target and background regions. A hyperspectral image (HSI) can serve as the source of data from which band groupings can be tested for optimality. The output of an adaptive cosine estimator target detection algorithm is used in an objective function. Three optimization algorithms are compared: particle swarm, dual annealing, and differential evolution. A global optimum is also found using a brute force approach on the Livermore Computing Syrah supercomputer. Three materials are investigated: calcite, gypsum, and limestone. This is done for 3-, 4-, and 5-band systems. The data originate from a longwave infrared HSI of a material display board. The optimization algorithms were run 30 times for every scenario. Performance statistics (maximum, minimum, mean, standard deviation, and median) based on the separation values are given. Additional characterization was performed using receiver operator characteristic (ROC) curves and the area under the ROC curve. While good performance was obtained for the three optimization algorithms, the dual annealing algorithm produced the highest and most consistent detection separation scores on average.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Deep learning for drag force modelling in dilute, poly-dispersed particle-laden flows with irregular-shaped particles

Here, this study applies machine learning-based approaches to develop a drag force model for irregular-shaped particles in incompressible flows. The particle-laden flows are studied through an in-house particle-resolved direct numerical simulation (PR-DNS) at low-intermediate Reynolds numbers (Re). We utilize the PR-DNS to obtain drag force coefficients and flow fields of single particles. A variational auto-encoder model is used to obtain latent vectors to represent the geometrical features of the particles, and artificial neural networks (ANN) are developed to predict drag force coefficients and flow fields of the single particles. This study applies a pairwise interaction extended point-particle (PIEP) model to obtain the coefficients assuming the flow fields of neighboring particles can be linearly superposed. The PIEP method shows significant improvement on prediction for a few neighbored particles. In addition, the results reveal R2 scores of 0.56-0.62 and errors of 9.1-10.0 % for the dilute, polydispersed systems with a volume fraction of 0.5 %.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Integrated Landscape Management to Reduce Biomass Feedstock Access Costs

Using geospatial, machine learning, and optimization techniques, a field scoring and design framework known as the bioenergy Landscape Environmental Assessment and Design System (bioLEADS) was developed and used to show through modelling that it is possible to reduce biomass feedstock access costs, improve field revenue, and shift intensive row crop production away from subfield areas susceptible to erosion and low in soil organic carbon. The purpose of bioLEADS is to automate agricultural field selection and subfield allocation to perennial energy crop production while incorporating reduced biomass feedstock access costs to support attaining the U.S. Department of Energy (DOE) Bioenergy Technologies Office (BETO) cost targets.

09 BIOMASS FUELS↗
DOE OSTI2026

Discovering Strong Gravitational Lenses in the Dark Energy Survey with Interactive Machine Learning and Crowd-sourced Inspection with Space Warps

We conduct a search for strong gravitational lenses in the Dark Energy Survey (DES) Year 6 imaging data. We implement a pre-trained Vision Transformer (ViT) for our machine learning (ML) architecture and adopt interactive machine learning to construct a training sample with multiple classes to address common types of false positives. Our ML model reduces ∼236 million DES cutout images to 22,564 targets of interest, including ∼85% of previously reported galaxy–galaxy lens candidates discovered in DES. These targets were visually inspected by citizen scientists, who ruled out ∼90% as false positives. Of the remaining 2618 candidates, 149 were expert-classified as “definite” lenses and 516 as “probable” lenses, for a total of 665 systems, with 147 of these candidates being newly identified. Additionally, we trained a second ViT to find double-source plane lens systems, finding at least one double-source system. Our main ViT excels at identifying galaxy–galaxy lenses, consistently assigning high scores to candidates with high expert assessments. The top 800 ViT-scored images include ∼100 of our “definite” lens candidates. This selection is an order of magnitude higher in purity than previous convolutional neural-network-based lens searches and demonstrates the feasibility of applying our methodology for discovering large samples of lenses in future surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

An Information Theoretic Approach to Identify Dominant Voltage Influencers for Unbalanced Distribution Systems

Smart distribution grid with multiple renewable energy sources can experience random voltage fluctuations due to variable generation, which may result in voltage violations. Traditional voltage control algorithms are inadequate to handle fast voltage variations. Therefore, new dynamic control methods are being developed that can significantly benefit from the knowledge of dominant voltage influencer (DVI) nodes. DVI nodes for a particular node of interest refer to nodes that have a relatively high impact on the voltage fluctuations at that node. Conventional power flow-based algorithms to identify DVI nodes are computationally complex, which limits their use in real-time applications. This paper proposes a novel information theoretic voltage influencing score (VIS) that quantifies the voltage influencing capacity of nodes with DERs/active loads in a three phase unbalanced distribution system. VIS is then employed to rank the nodes and identify the DVI set. VIS is derived analytically in a computationally efficient manner and its efficacy to identify DVI nodes is validated using the IEEE 37-node test system. It is shown through experiments that KL divergence and Bhattacharyya distance are effective indicators of DVI nodes with an identifying accuracy of more than 90%. Additionally, the computation burden is also reduced by an order of 5, thus providing the foundation for efficient voltage control.

42 ENGINEERING↗
DOE OSTI2025

Low-cost buffer storage for solar industrial steam applications

Motivation and importance: Many industrial process heat consumers operate throughout the day, and deep decarbonization with solar energy requires thermal energy storage. Thermal energy storage provides an additional operational benefit of firm delivery of steam – essential to reducing the risk of costly production interruptions. The group of process heat consumers that use steam for biofuels production for the California transportation fuels market is incentivized through the Low Carbon Fuel Standard (LCFS) to reduce their carbon intensity through the use of solar steam. Examples include ethanol and dimethyl ether production. The production of these and other fuels involves distillation driven by steam reboilers with steam pressures in the range 100-280 psig. These pressures can be delivered from the type of thermal energy storage that has been the focus of this project – a steam accumulator Sunvapor, Inc. (SVI) calls a “Bullet Steam Accumulator” or “BSA TM ”. Our project adds to the understanding of the design and integration of steam accumulators charged by solar thermal energy in a biofuels production facility, and similar facilities that consume process steam. The fuel application requires a demanding level of Process Safety Management not found in other steam applications. The type of steam accumulator SVI engineered and built integrates steam generation and storage in one vessel to reduce cost. Achieving a first of a kind permit to operate such a first device has also added to the understanding of the industrial application of such systems. Goals: The overall goal of the project has been to demonstrate the advantages that a “Bullet Steam Accumulator” (BSA™) can provide to solar steam integration with a manufacturing process. The industrial host has already been convinced of the potential of the available solar steam to lower than carbon intensity score. They have been interviewed for a number of publications such as that tout the advantages SVI’s system brings to their operations. Accomplishments: At the end of 2023 Sunvapor placed in service the country’s first industrial solar boiler with thermal energy storage. At the same time, this placement in service represents the first pilot commercial system to decarbonize biofuels production through the use of solar thermal energy. While the full technical effectiveness will be shown as soon as the customer’s plant is able to consume the available steam, the economic feasibility was indicated early in the project when the combination of costs and steam pricing required to meet investor expectations was determined. The project represents a reference pilot from which a full-scale commercial system may be expanded. This pilot is a necessary first step in providing the full potential benefits to the public, including decarbonization of transportation fuels. The decarbonization of the separation steps in fuels production may be the most cost-effective and nearest term solar fuel. Co-benefits include a reduction of air pollutants such as NOx and particulate matter, and job creation for the construction, operation and maintenance of industrial solar facilities.

14 SOLAR ENERGY↗
DOE OSTI2024

Modeling performance of data collection systems for high-energy physics

Exponential increases in scientific experimental data are outpacing silicon technology progress, necessitating heterogeneous computing systems—particularly those utilizing machine learning (ML)—to meet future scientific computing demands. The growing importance and complexity of heterogeneous computing systems require systematic modeling to understand and predict the effective roles for ML. We present a model that addresses this need by framing the key aspects of data collection pipelines and constraints and combining them with the important vectors of technology that shape alternatives, computing metrics that allow complex alternatives to be compared. For instance, a data collection pipeline may be characterized by parameters such as sensor sampling rates and the overall relevancy of retrieved samples. Alternatives to this pipeline are enabled by development vectors including ML, parallelization, advancing CMOS, and neuromorphic computing. By calculating metrics for each alternative such as overall F1 score, power, hardware cost, and energy expended per relevant sample, our model allows alternative data collection systems to be rigorously compared. We apply this model to the Compact Muon Solenoid experiment and its planned high luminosity-large hadron collider upgrade, evaluating novel technologies for the data acquisition system (DAQ), including ML-based filtering and parallelized software. The results demonstrate that improvements to early DAQ stages significantly reduce resources required later, with a power reduction of 60% and increased relevant data retrieval per unit power (from 0.065 to 0.31 samples/kJ). However, we predict that further advances will be required in order to meet overall power and cost constraints for the DAQ.

Olin-Ammentorp, Wilkie (ORCID:0000000224729862)↗
DOE OSTI2024

Analyzing Potential Failures and Effects in a Pilot-Scale Biomass Preprocessing Facility for Improved Reliability

This study demonstrates a failure identification methodology applied to a preprocessing facility generating conversion-ready feedstocks from biomass meeting conversion process critical quality attribute (CQA) specifications. Failure Modes and Effects Analysis (FMEA) was used as an industrially relevant risk analysis approach to evaluate a logging residue preprocessing system to prepare feedstock for pyrolysis conversion. Risk evaluations considered both system-level and operation unit-level assessments considering process efficiency, product quality, cost, sustainability, and safety. Key outputs included estimations of semi-quantitative risk scores for each failure, identification of the failure impacts, identification of failure causes associated with material attributes and process parameters, ranking success rates of failure detection methods, and speculation of potential mitigation strategies for decreasing failure risk scores. Results showed that deviations from moisture specifications had cascading consequences for other CQAs along with process safety implications. Failures linked to fixed carbon specifications carried the highest risk scores for product quality and process efficiency impacts. As increased throughput can be inversely related to meeting product quality specifications; achieving throughput and other material-based CQAs simultaneously will likely require system optimization or prioritization based on system economics. Ultimately, this work successfully demonstrates FMEA as a risk analysis approach for other bioenergy process systems.

09 BIOMASS FUELS↗
DOE OSTI2023

Final Technical Report

Statement of the problem or situation that is being addressed in your application. The DOE and its national laboratories developed the Home Energy Score™ (HES) to encourage homeowners to improve their energy performance, lower costs and to share energy information through the MLS listing, appraisal, and financing channels. While the HES is an instrumental tool, it is currently underutilized and consists of technical, structural and sector barriers which need to be addressed in order to scale and many energy efficiency contractors are understandably overwhelmed by the added time and effort and lack of incentive to sell and deliver deep retrofit projects while simultaneously meeting the DOE HES program requirements; consequently, contractors may decide to forgo participation. Home Energy Rating System (HERS) Raters have the opportunity to play the critical Assessor role in producing a Home Energy Score (HES); this role has immense potential but currently is unfulfilled. Lastly, while utilities are interested in their customer base achieving greater energy efficiency, especially to help offset growing residential loads in states like California that are accelerating electrification, utilities do not have access to the market actors who are on the front line of influence to homeowners or review and approve their permits: HERS Raters, assessors, contractors and building departments. General statement of how this problem is being addressed: ConSol will integrate the Home Energy Score™ (HES) to its State of California, approved home energy rating services (HERS) platform (CHEERS) to develop a single tool for contractors nationwide to assess, record and install recommended cost, energy, and emissions saving measures to the 140 million single-family homes throughout the U.S. and 14 million homes in California (CHEERS+HES). The CHEERS high fidelity energy code permitting data will be integrated with HES for simple, accurate, easy-to-use home energy estimation and analysis and will directly gain access to the retrofit and renovations markets with the same upgraded platform. This innovative project will assist the utilities in supporting existing homes in their jurisdictions with HES and develop measures to improve energy efficiency and reduce emissions. How is this problem being addressed? What is the overall project approach? In effort to expand the Home Energy Score™ (HES) by increasing the use of aggregable home energy asset data, ConSol proposes to integrate the DOE HES via Application Programming Interface (API) to its State of California approved home energy rating services platform (CHEERS). Once the CHEERS platform and HES are integrated (CHEERS+HES), this enhanced platform will be instantly available and actively deployed via Phase 1 pilot to HERS Raters, assessors and contractors in California to market-test the solution, understand the rate of adoption and identify opportunities for improvement prior to scaling nationally. The CHEERS high fidelity energy code permitting data will be integrated with HES for simple, accurate, easy-to-use home energy estimation and analysis and will directly gain access to the retrofit and renovations markets with the same upgraded platform. This innovative project will assist the building industry and homeowners with an easy-to-use assessment if energy and carbon impacts of existing homes, and assist the utilities in supporting existing homes in their jurisdictions with HES to improve energy efficiency and reduce emissions. What is to be done in Phase I? During Phase I of this proposed project, ConSol will (1) design software architecture that links CHEERS to the Home Energy ScoreTM via API, (2) solicit partnership from one or more California utilities for a regional pilot, (3) test the new software with its HERS Raters and contractor network in the partnership utility jurisdiction, (4) launch a pilot version of the newly developed software with HERS Raters and contractors in the utility territory, and (5) explore California’s GoGreen energy efficiency homeowner lending program in parallel with the pilot. Commercial Applications and Other Benefits. Summarize the future applications or public benefits if the project is carried over into Phase II or Phase III and beyond. The CHEERS+HES commercialized product will be ready for national market scale following a successful Phase 1 performance. The CHEERS+HES adoption is estimated to reach a 5% adoption growth rate versus the 110,000 baseline, starting in Year 1 after Phase I completion, and continuing each year. As a direct benefit to the DOE, CHEERS will set a goal of 100,000 Home Energy Score assessments for existing home alterations within the first 10 years following Phase 1 performance. The technical benefits of this proposed project include the harmonized, automated, and seamless integration of the DOE HES into the widely used and market leading California energy registry, CHEERS. The social benefits include the aggregate energy, cost and GHG savings by allowing the broader public streamlined access to the CHEERS+HES measurement and the energy efficiency recommended measures that may result. Key Words: Home Energy ScoreTM (HES); Application Programming Interface (API); Home Energy Rating Services (HERS); HERS Raters; contractors; assessors; existing homes, energy asset data; cost, energy, and emissions saving measures; energy code (Title 24) compliance; document repository; utilities; pilot; newly developed software; energy efficiency; homeowner. Summary for Members of Congress: The DOE Home Energy Score™ (HES) is a tool to encourage homeowners to improve their energy performance, lower costs and share energy information but is underutilized and consists of barriers which need to be addressed in order to scale. In effort to expand the HES, CHEERS, Inc. will integrate the HES to its State of California, approved home energy rating services (HERS) platform (CHEERS) to develop a single tool for contractors nationwide to assess, record and install recommended cost, energy, and emissions saving measures to the 140 million single-family homes throughout the U.S. and 14 million homes in California.

Application Programming Interface (API)↗
DOE OSTI2024

Ice Phase Classification Made Easy with Score-Based Denoising

Accurate identification of ice phases is essential for understanding various physicochemical phenomena. However, such classification for structures simulated with molecular dynamics is complicated by the complex symmetries of ice polymorphs and thermal fluctuations. For this purpose, both traditional order parameters and data-driven machine learning approaches have been employed, but they often rely on expert intuition, specific geometric information, or large training data sets. In this work, we present an unsupervised phase classification framework that combines a score-based denoiser model with a subsequent model-free classification method to accurately identify ice phases. Further, the denoiser model is trained on perturbed synthetic data of ideal reference structures, eliminating the need for large data sets and labeling efforts. The classification step utilizes the smooth overlap of atomic position (SOAP) descriptors as the atomic fingerprint, ensuring Euclidean symmetries and transferability to various structural systems. Our approach achieves a remarkable 100% accuracy in distinguishing ice phases of test trajectories using only seven ideal reference structures of ice phases as model inputs. This demonstrates the generalizability of the score-based denoiser model in facilitating phase identification for complex molecular systems. The proposed classification strategy can be broadly applied to investigate structural evolution and phase identification for a wide range of materials, offering new insights into the fundamental understanding of water and other complex systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗