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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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At least 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

RNA-Dependent Cysteine Biosynthesis in Bacteria and Archaea

ABSTRACT The diversity of the genetic code systems used by microbes on earth is yet to be elucidated. It is known that certain methanogenic archaea employ an alternative system for cysteine (Cys) biosynthesis and encoding; tRNA Cys is first acylated with phosphoserine (Sep) by O -phosphoseryl-tRNA synthetase (SepRS) and then converted to Cys-tRNA Cys by Sep-tRNA:Cys-tRNA synthase (SepCysS). In this study, we searched all genomic and metagenomic protein sequence data in the Integrated Microbial Genomes (IMG) system and at the NCBI to reveal new clades of SepRS and SepCysS proteins belonging to diverse archaea in the four major groups (DPANN, Euryarchaeota , TACK, and Asgard) and two groups of bacteria (“ Candidatus Parcubacteria” and Chloroflexi ). Bacterial SepRS and SepCysS charged bacterial tRNA Cys species with cysteine in vitro . Homologs of SepCysE, a scaffold protein facilitating SepRS⋅SepCysS complex assembly in Euryarchaeota class I methanogens, are found in a few groups of TACK and Asgard archaea, whereas the C-terminally truncated homologs exist fused or genetically coupled with diverse SepCysS species. Investigation of the selenocysteine (Sec)- and pyrrolysine (Pyl)-utilizing traits in SepRS-utilizing archaea and bacteria revealed that the archaea carrying full-length SepCysE employ Sec and that SepRS is often found in Pyl-utilizing archaea and Chloroflexi bacteria. We discuss possible contributions of the SepRS-SepCysS system for sulfur assimilation, methanogenesis, and other metabolic processes requiring large amounts of iron-sulfur enzymes or Pyl-containing enzymes. IMPORTANCE Comprehensive analyses of all genomic and metagenomic protein sequence data in public databases revealed the distribution and evolution of an alternative cysteine-encoding system in diverse archaea and bacteria. The finding that the SepRS-SepCysS-SepCysE- and the selenocysteine-encoding systems are shared by the Euryarchaeota class I methanogens, the Crenarchaeota AK8/W8A-19 group, and an Asgard archaeon suggests that ancient archaea may have used both systems. In contrast, bacteria may have obtained the SepRS-SepCysS system from archaea. The SepRS-SepCysS system sometimes coexists with a pyrrolysine-encoding system in both archaea and bacteria. Our results provide additional bioinformatic evidence for the contribution of the SepRS-SepCysS system for sulfur assimilation and diverse metabolisms which require vast amounts of iron-sulfur enzymes and proteins. Among these biological activities, methanogenesis, methylamine metabolism, and organohalide respiration may have local and global effects on earth. Taken together, uncultured bacteria and archaea provide an expanded record of the evolution of the genetic code.

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

Battery Lifecycle Framework

The Battery Lifecycle (BLC) Framework is an open-source platform that provides tools to visualize and share battery data from material characterization, cell testing, manufacturing, and field testing through the technology development cycle. BLC has three components: data importers, a front-end for querying the data and creating visualizations, and an application programming interface to provide access to the data from Python. BLC has been deployed for tracking the development of a battery from the lab to a manufacturing line and systems installed in the field and for comparing studies of multiple cells of the same battery chemistry and configuration. The code was developed around Redash, a robust open-source extract-transform-load engine. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525. SAND2021-4546 O

De Angelis, Valerio↗
DOE OSTI2015

Development of an Advanced Stimulation / Production Predictive Simulator for Enhanced Geothermal Systems

There are several well-known obstacles to the successful deployment of EGS projects on a commercial scale, of course. EGS projects are expected to be deeper, on the average, than conventional “natural” geothermal reservoirs, and drilling costs are already a formidable barrier to conventional geothermal projects. Unlike conventional resources (which frequently announce their presence with natural manifestations such as geysers, hot springs and fumaroles), EGS prospects are likely to appear fairly undistinguished from the earth surface. And, of course, the probable necessity of fabricating a subterranean fluid circulation network to mine the heat from the rock (instead of simply relying on natural, pre-existing permeable fractures) adds a significant degree of uncertainty to the prospects for success. Accordingly, the basic motivation for the work presented herein was to try to develop a new set of tools that would be more suitable for this purpose. Several years ago, the Department of Energy’s Geothermal Technologies Office recognized this need and funded a cost-shared grant to our company (then SAIC, now Leidos) to partner with Geowatt AG of Zurich, Switzerland and undertake the development of a new reservoir simulator that would be more suitable for EGS forecasting than the existing tools. That project has now been completed and a new numerical geothermal reservoir simulator has been developed. It is named “HeatEx” (for “Heat Extraction”) and is almost completely new, although its methodology owes a great deal to other previous geothermal software development efforts, including Geowatt’s “HEX-S” code, the STAR and SPFRAC simulators developed here at SAIC/Leidos, the MINC approach originally developed at LBNL, and tracer analysis software originally formulated at INEL. Furthermore, the development effort was led by engineers with many years of experience in using reservoir simulation software to make meaningful forecasts for real geothermal projects, not just software designers. It is hoped that, as a result, HeatEx will prove useful during the early stages of the development of EGS technology. The basic objective was to design a tool that could use field data that are likely to become available during the early phases of an EGS project (that is, during initial reconnaissance and fracture stimulation operations) to guide forecasts of the longer-term behavior of the system during production and heat-mining.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

Foundations of plasma standards

Abstract The field of low-temperature plasmas (LTPs) excels by virtue of its broad intellectual diversity, interdisciplinarity and range of applications. This great diversity also challenges researchers in communicating the outcomes of their investigations, as common practices and expectations for reporting vary widely in the many disciplines that either fall under the LTP umbrella or interact closely with LTP topics. These challenges encompass comparing measurements made in different laboratories, exchanging and sharing computer models, enabling reproducibility in experiments and computations using traceable and transparent methods and data, establishing metrics for reliability, and in translating fundamental findings to practice. In this paper, we address these challenges from the perspective of LTP standards for measurements, diagnostics, computations, reporting and plasma sources. This discussion on standards, or recommended best practices, and in some cases suggestions for standards or best practices, has the goal of improving communication, reproducibility and transparency within the LTP field and fields allied with LTPs. This discussion also acknowledges that standards and best practices, either recommended or at some point enforced, are ultimately a matter of judgment. These standards and recommended practices should not limit innovation nor prevent research breakthroughs from having real-time impact. Ultimately, the goal of our research community is to advance the entire LTP field and the many applications it touches through a shared set of expectations.

data and reaction mechanisms↗
DOE OSTI2019

Comparative genomics reveals complex natural product biosynthesis capacities and carbon metabolism across host‐associated and free‐living Aquimarina ( Bacteroidetes, Flavobacteriaceae ) species

Summary This study determines the natural product biosynthesis and full coding potential within the bacterial genus Aquimarina . Using comprehensive phylogenomics and functional genomics, we reveal that phylogeny instead of isolation source [host‐associated (HA) vs. free‐living (FL) habitats] primarily shape the inferred metabolism of Aquimarina species. These can be coherently organized into three major functional clusters, each presenting distinct natural product biosynthesis profiles suggesting that evolutionary trajectories strongly underpin their secondary metabolite repertoire and presumed bioactivities. Aquimarina spp. are highly versatile bacteria equipped to colonize HA and FL microniches, eventually displaying opportunistic behaviour, owing to their shared ability to produce multiple glycoside hydrolases from diverse families. We furthermore uncover previously underestimated, and highly complex secondary metabolism for the genus by detecting 928 biosynthetic gene clusters (BGCs) across all genomes, grouped in 439 BGC families, with polyketide synthases (PKSs), terpene synthases and non‐ribosomal peptide synthetases (NRPSs) ranking as the most frequent BGCs encoding drug‐like candidates. We demonstrate that the recently described cuniculene ( trans ‐AT PKS) BGC is conserved among, and specific to, the here delineated A. megaterium‐macrocephali ‐ atlantica phylogenomic clade. Our findings provide a timely and in‐depth perspective of an under‐explored yet emerging keystone taxon in the cycling of organic matter and secondary metabolite production in marine ecosystems.

Silva, Sandra G.↗
DOE OSTI2024

FORCE Update 2024

The Framework for Optimization of Resources and Economics (FORCE) tool suite is the U.S. Department of Energy’s Nuclear Integrated Energy Systems (IES) Program flagship tool suite for technoeconomic IES analysis of IES. This tool suite is useful for analysis designed to evaluate and improve the technoeconomics of energy production systems, particularly for systems including nuclear technology. In this report, we document the development activity for the FORCE tool suite to extend its capabilities as performed during fiscal year 2024. In addition to reliability and accessibility, capability is one of the three standards guiding the development of the FORCE tool suite and the software codes that are its constituent parts. Extending the capabilities of the FORCE tool suite allows analysis both within the IES program as well as industry, university, and laboratory partners to perform analysis with more accuracy, insight, and impactful narrative. Four areas of capability development were the focus of activity this year: economic parameter uncertainty quantification, multiresolution analysis, components-to-optimization workflow automation, and statespace construction workflows for real-time optimal control. In economic parameter uncertainty quantification, the ability of HERON to capture risk due to scenarios (weather and energy demand uncertainty) was expanded to also include uncertainties in financial parameters such as capital cost or operation and maintenance costs. By including these sources of uncertainty, which are sometimes very large compared with scenario uncertainty, HERON is better able to capture the risk posed by investment in various IES technology. Because of this, analysts can also consider the reduction in risks that can be realized by choice of some technologies. In multiresolution analysis, development activity extended on work completed previously. In fiscal year 2023, methods for decomposing time series signals, such as demand, solar and wind availability, and price profiles, were analyzed and down-selected to those most effective at splitting signals into different resolutions. These resolutions allow considering the influence of different energy demand and supply behaviors across different time scales. For example, energy demand might be divided into seasonal, weekly, and hourly profiles. In fiscal year 2024, this preliminary work was extended and implemented within the Risk Analysis Virtual Environment (RAVEN) risk and uncertainty analysis platform, which is used throughout the FORCE framework. This development of the “multi-resolution time series analysis” (MR-TSA) module in RAVEN allows training synthetic history generators on complex time series. These synthetic history generators can then be used in HERON for generating scenarios that represent possible market and weather scenarios that can be analyzed on different time scales. We envision completing this work in the future, implementing multiresolution dispatch optimization strategies that can make the most beneficial use of these stratified time histories. In components-to-optimization workflow development, workflows for translating user inputs of components into algorithms for algebraic optimization were selected and implemented. Similar algorithms within the Holistic Energy Resource Optimization Network (HERON) were separated from the main code base of HERON and gathered with the components-to-optimization workflows in the new Dispatch Optimization Variable Engine (DOVE) software library. This modularization allows FORCE users to analyze dispatch optimization and energy system duty cycles independently of HERON, which previously was a burdensome task. Additionally, these dispatch optimization algorithms, set up in an independent library, can now be used across all software applications within FORCE, especially including the real-time optimal control software Optimization of Real-time Capacity Allocation (ORCA). Allowing FORCE software to share dispatch optimization algorithms within a single library allows for improved software maintenance and reliability. In statespace characterization workflow development, alternative workflows for optimizing dispatch with additional technical accuracy was the focus, particularly to improve the real-time optimization decision making in ORCA. Using algorithms and workflows initially developed for the Feasible Actuator Range Modifier (FARM), workflows for determining the statespace representation of IES were identified and demonstrated. The resulting dispatch optimization required a more robust optimization algorithm than that originally used in HERON (and moved to DOVE), which required adding an alternate workflow to DOVE that can more accurately match the behavior of physical systems using a partial differential equation representation. In conclusion, capability developments in the FORCE tool suite in fiscal year 2024 have improved the ability of the FORCE tool suite to perform

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Data and code for Daily and Multi-Day Extreme Rainfall Analysis Under Future Climates Using Stochastic Storm Transposition and NEX-GDDP-CMIP6 Over CONUS

This data package provides inputs, codes, and outputs for a comprehensive analysis of projected changes in extreme precipitation across 10 regions of the continental United States, using 34 downscaled Earth System Models (ESMs) from the NASA Earth Exchange Global Daily Downscaled Projections, Coupled Model Intercomparison Project Phase 6 (NEX-GDDP-CMIP6) dataset. These models are part of the Coupled Model Intercomparison Project Phase 6 (CMIP6), a coordinated climate modeling framework widely used to assess climate change impacts. The analysis applies a stochastic storm transposition method to quantify changes in extreme rainfall under two Shared Socioeconomic Pathway (SSP) climate scenarios—SSP2-4.5 (moderate emissions) and SSP5-8.5 (high emissions)—compared to historical conditions (1995–2014 vs. 2081–2100). The dataset includes rainfall depth estimates for extreme events with return periods from 2 to 500 years across multiple storm durations (1, 3, and 5 days) for each of the 10 U.S. regions. Weighted ensemble statistics are derived from individual ESM performance against historical precipitation patterns, enabling robust uncertainty quantification through both sign-based and permutation-test-based model agreement assessments. Key analyses address: (1) relative changes in extreme precipitation for each climate scenario, (2) differences between SSP scenarios (SSP5-8.5 vs. SSP2-4.5), (3) contrasts between rare and frequent events, and (4) variations between multi-day and daily storm durations. The workflow produces ensemble statistics—median, 5th, 25th, 75th, and 95th percentiles—along with model agreement metrics that identify regions and event types with robust climate change signals. The dataset includes: processed rainfall depth outputs (netCDF format) from the RainyDay Python package, ESM weights from historical performance evaluation using DayMet observations, ensemble statistics across all storm dimensions, and figures summarizing key findings.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Thermal-Hydraulic design of a Fluoride High-Temperature Demonstration Reactor

The Fluoride salt-cooled High-temperature Reactor (FHR) 'Demonstration Reactor' (DR) is a novel reactor concept using molten salt coolant and tri-structural isotropic (TRISO) fuel that is being investigated at Oak Ridge National Laboratory (ORNL). The objective of the FHR DR is to advance the technology readiness level of FHRs. The FHR DR will demonstrate the technologies needed to close remaining gaps to commercial viability. The FHR DR will have a thermal power of 100 MWt, very similar to the Small modular Advanced High Temperature Reactor (SmAHTR), another FHR ORNL concept with a power of 125 MWt. The FHR DR shares features with the ORNL Advanced High Temperature Reactor (AHTR), which has a power of 3400 MWt and is cooled by a molten salt. The FHR DR combines three existing technologies: (1) high-temperature, low-pressure molten salt coolant, (2) high-temperature coated particle TRISO fuel, and (3) passive decay heat cooling systems using direct reactor auxiliary cooling systems (DRACS). This paper presents FHR DR thermal-hydraulic design calculations. The FHR DR design concept is being investigated by ORNL. Thermal-hydraulic calculations performed with the codes COMSOL and RELAP5-3D were used to determine the potential thermal and hydraulic characteristics of the concept. These results show that the FHR DR should be able to demonstrate the efficacy of the passive decay heat removal using DRACS, which is a key safety feature in a number of proposed FHR designs. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2020

Technical Qualification of New Materials for High Efficiency Coal-Fired Boilers and Other Advanced FE Concepts: Haynes® 282® ASME Boiler and Pressure Vessel Code Case

This DOE Fossil Energy project, FEAA117, addressed materials issues relevant to qualifying and deploying a Ni-base alloy for a new application in an advanced ultra-supercritical coal-fired boiler. The goal was the deployment of Haynes International alloy Haynes® 282® for applications in superheaters, reheaters, and steam delivery pipes, by completing base metal, cross-weld and all-weld metal mechanical testing needed for an ASME Boiler and Pressure Vessel Code Case and the associated microstructural analyses needed for assurance of boiler-relevant lifetimes. The alloy also is of interest for other applications including supercritical CO₂ power generation cycles. Tensile testing was completed from 20°-927°C on the three base metal alloy 282 heats, two sets of cross-weld specimens and all weld metal specimens. A total of 148 creep tests with over 565,000 h of cumulative testing was completed on base metal, cross-weld and all weld metal specimens. This project was performed with cost share from Haynes International.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2021

Data Arrays for Microearthquake (MEQ) Monitoring using Deep Learning for the Newberry EGS Sites

The 'Machine Learning Approaches to Predicting Induced Seismicity and Imaging Geothermal Reservoir Properties' project looks to apply machine learning (ML) methods to Microearthquake (MEQ) data for imaging geothermal reservoir properties and forecasting seismic events, in order to advance geothermal exploration and safe geothermal energy production. As part of the project, this submission provides data arrays for 149 microearthquakes between the year 2012 and 2013 at the Newberry EGS Site for use with the Deep Learning Algorithm that has been developed. The data provided includes raw waveform data, location data, normalized waveform data, and processed waveform data. Penn State Geothermal Team has shared the following files from the project: - 149 microearthquakes (MEQs) between 2012 and 2013 at Newberry EGS sites, 'Normalized Waveform Inputs.npz' are normalized waveforms. - labels of 149 MEQs: Processed Waveform Inputs.npz - location labels of 149 MEQs: Location Data.npz Note: .npz is the python file format by NumPy that provides storage of array data.

15 GEOTHERMAL ENERGY↗
DOE OSTI2017

ALFA Shared Autonomy Manipulation Data with a Seabotix vLBV300

This report outlines marine field demonstrations for manipulation tasks with a semi-Autonomous Underwater Vehicle (sAUV). The vehicle is built off a Seabotix vLBV300 platform with custom software interfacing it with the Robot Operating System (ROS). The vehicle utilizes an inertial navigation system available from Greensea Systems, Inc. based on a Gladiator Landmark 40 IMU coupled with a Teledyne Explorer Doppler Velocity Log to perform station keeping at a desired location and orientation. We performed two marine trials with the vehicle: a near-shore shared autonomy manipulation trial and an offshore attempted intervention trial. These demonstrations were designed to show the capabilities of our sAUV system for inspection and basic manipulation tasks in real marine environments.

16 TIDAL AND WAVE POWER↗
DOE OSTI2023

The genetics of aerotolerant growth in an alphaproteobacterium with a naturally reduced genome

Reduced genome bacteria are genetically simplified systems that facilitate biological study and industrial use. The free-living alphaproteobacterium Zymomonas mobilis has a naturally reduced genome containing fewer than 2,000 protein-coding genes. Despite its small genome, Z. mobilis thrives in diverse conditions including the presence or absence of atmospheric oxygen. However, insufficient characterization of essential and conditionally essential genes has limited broader adoption of Z. mobilis as a model alphaproteobacterium. Here, we use genome-scale CRISPRi-seq (clustered regularly interspaced short palindromic repeats interference sequencing) to systematically identify and characterize Z. mobilis genes that are conditionally essential for aerotolerant or anaerobic growth or are generally essential across both conditions. Comparative genomics revealed that the essentiality of most “generally essential” genes was shared between Z. mobilis and other Alphaproteobacteria, validating Z. mobilis as a reduced genome model. Among conditionally essential genes, we found that the DNA repair gene, recJ, was critical only for aerobic growth but reduced the mutation rate under both conditions. Further, we show that genes encoding the F1FO ATP synthase and Rhodobacter nitrogen fixation (Rnf) respiratory complex are required for the anaerobic growth of Z. mobilis. Combining CRISPRi partial knockdowns with metabolomics and membrane potential measurements, we determined that the ATP synthase generates membrane potential that is consumed by Rnf to power downstream processes. Rnf knockdown strains accumulated isoprenoid biosynthesis intermediates, suggesting a key role for Rnf in powering essential biosynthetic reactions. Our work establishes Z. mobilis as a streamlined model for alphaproteobacterial genetics, has broad implications in bacterial energy coupling, and informs Z. mobilis genome manipulation for optimized production of valuable isoprenoid-based bioproducts.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Partnership For Edge Physics Simulation

In this effort, we will extend our prior work as part of CPES (i.e., DART and DataSpaces) to support in-situ tight coupling between application codes that exploits data locality and core-level parallelism to maximize on-chip data exchange and reuse. This will be accomplished by mapping coupled simulations so that the data exchanges are more localized within the nodes. Coupled simulation workflows can more effectively utilize the resources available on emerging HEC platforms if they can be mapped and executed to exploit data locality as well as the communication patterns between application components. Scheduling and running such workflows requires an extended framework that should provide a unified hybrid abstraction to enable coordination and data sharing across computation tasks that run on the heterogeneous multi-core-based systems, and develop a data-locality based dynamic tasks scheduling approach to increase on-chip or intra-node data exchanges and in-situ execution. This effort will extend our prior work as part of CPES (i.e., DART and DataSpaces), which provided a simple virtual shared-space abstraction hosted at the staging nodes, to support application coordination, data sharing and active data processing services. Moreover, it will transparently manage the low-level operations associated with the inter-application data exchange, such as data redistributions, and will enable running coupled simulation workflow on multi-cores computing platforms.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

ChargeX OCPI Recommendations

The Open Charge Point Interface (OCPI) is an open protocol that enables electric vehicle (EV) charging systems to work together across networks. It supports communication and data sharing between Charge Point Operators (CPOs), who manage charging stations, and e-Mobility Service Providers (eMSPs), who provide charging services to EV drivers. OCPI facilitates functions like user authorization, remote charge point control, charging session data exchange, and billing through Charge Detail Records (CDRs). This allows EV roaming, so drivers can charge at different networks without multiple accounts. As the EV market grows due to increased adoption and technological advancements, OCPI faces higher demands. This has revealed issues with CDR format consistency, timestamp standardization across regions, transmission of EV-side error codes for troubleshooting, and support for new use cases. These challenges can affect operations and user experience, particularly as the industry starts considering Vehicle-to-Grid (V2G) systems, where EVs supply energy to the grid, and Vehicle-to-Everything (V2X) technologies for broader energy interactions. Using feedback from the ChargeX Diagnostics taskforce discussions, industry 1-on-1 meetings, technical standards, and OCPI’s evolution through versions (e.g., OCPI 2.1.1, 2.2, and 2.2.1), this report identifies these issues and suggests practical recommendations. These aim to improve interoperability, streamline operations, and prepare OCPI for future trends in the EV charging ecosystem.

32 - ENERGY CONSERVATION, CONSUMPTION, AND UTILIZA↗
DOE OSTI2018

TopoMS: Comprehensive topological exploration for molecular and condensed-matter systems

We introduce TopoMS, a computational tool enabling detailed topological analysis of molecular and condensed-matter systems, including the computation of atomic volumes and charges through the quantum theory of atoms in molecules, as well as the complete molecular graph. With roots in techniques from computational topology, and using a shared-memory parallel approach, TopoMS provides scalable, numerically robust, and topologically consistent analysis. TopoMS can be used as a command-line tool or with a GUI (graphical user interface), where the latter also enables an interactive exploration of the molecular graph. This paper presents algorithmic details of TopoMS and compares it with state-of-the-art tools: Bader charge analysis v1.0 (Arnaldsson et al., 01/11/17) and molecular graph extraction using Critic2 (Otero-de-la-Roza et al., Comput. Phys. Commun. 2014, 185, 1007). TopoMS not only combines the functionality of these individual codes but also demonstrates up to 4× performance gain on a standard laptop, faster convergence to fine-grid solution, robustness against lattice bias, and topological consistency. TopoMS is released publicly under BSD License.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Synergizing human expertise and AI efficiency with language model for microscopy operation and automated experiment design

With the advent of large language models (LLMs), in both the open source and proprietary domains, attention is turning to how to exploit such artificial intelligence (AI) systems in assisting complex scientific tasks, such as material synthesis, characterization, analysis and discovery. Here, we explore the utility of LLMs, particularly ChatGPT4, in combination with application program interfaces (APIs) in tasks of experimental design, programming workflows, and data analysis in scanning probe microscopy, using both in-house developed APIs and APIs given by a commercial vendor for instrument control. We find that the LLM can be especially useful in converting ideations of experimental workflows to executable code on microscope APIs. Beyond code generation, we find that the GPT4 is capable of analyzing microscopy images in a generic sense. At the same time, we find that GPT4 suffers from an inability to extend beyond basic analyses for more in-depth technical experimental design. We argue that an LLM specifically fine-tuned for individual scientific domains can potentially be a better language interface for converting scientific ideations from human experts to executable workflows. Such a synergy between human expertise and LLM efficiency in experimentation can open new doors for accelerating scientific research, enabling effective experimental protocols sharing in the scientific community.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Multimodal representation learning for predicting molecule–disease relations

Motivation: Predicting molecule–disease indications and side effects is important for drug development and pharmacovigilance. Comprehensively mining molecule–molecule, molecule–disease and disease–disease semantic dependencies can potentially improve prediction performance. Methods: We introduce a Multi-Modal REpresentation Mapping Approach to Predicting molecular-disease relations (M2REMAP) by incorporating clinical semantics learned from electronic health records (EHR) of 12.6 million patients. Specifically, M2REMAP first learns a multimodal molecule representation that synthesizes chemical property and clinical semantic information by mapping molecule chemicals via a deep neural network onto the clinical semantic embedding space shared by drugs, diseases and other common clinical concepts. To infer molecule–disease relations, M2REMAP combines multimodal molecule representation and disease semantic embedding to jointly infer indications and side effects. Results: We extensively evaluate M2REMAP on molecule indications, side effects and interactions. Results show that incorporating EHR embeddings improves performance significantly, for example, attaining an improvement over the baseline models by 23.6% in PRC-AUC on indications and 23.9% on side effects. Further, M2REMAP overcomes the limitation of existing methods and effectively predicts drugs for novel diseases and emerging pathogens. Availability and implementation: The code is available at https://github.com/celehs/M2REMAP, and prediction results are provided at https://shiny.parse-health.org/drugs-diseases-dev/.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Retro-Commissioning Sensor Suitcase for Energy Efficiency

This project will enable Pacific Northwest National Laboratory (PNNL) and Lawrence Berkeley National Laboratory (LBNL) to work with industry partner, GreenPath Energy Solutions, joint developers of the Suitcase, to enhance the capabilities and usability of the Retro-Commissioning Sensor Suitcase (hereinafter the Sensor Suitcase or Suitcase), assisting GreenPath to take the Suitcase to market and rapidly expand their market share. The proposed project will specifically focus on 1) adding sensors types to extend the data collection capability and support more building and equipment performance metrics and identification of even more energy saving opportunities, 2) developing algorithms to identify recommendations for the new energy savings opportunities from item 1 and to prototype software modifications implementing them, 3) validating the use of vibration sensors to detect the operating state of a broader set of packaged HVAC equipment (additional capacities, different numbers of stages, etc.) than tested in initial development and modifying the state algorithm and software code, as needed, 4) improving cost-effectiveness in manufacturing the Sensor Suitcase, and 5) additional field testing of the technology in real buildings to more comprehensively validate Suitcase performance and to guide refinement of its capabilities. Project results will position GreenPath, and potential future licensees, to implement the new capabilities developed in this project in GreenPath’s RCx Building Suitcase, increasing its functionality and the savings resulting from its use. These enhancements will increase the value of the Suitcase to users and increase the potential market for its use and impacts in that impact.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗