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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Cadillac CT6 Super Cruise On-road Data

This dataset encompasses about 60 individual drives of a 2019 Cadillac CT6 with Super Cruise in its relevant operational domain covering more than 1,000 miles. Adhering to the limited operational design domain of the investigated version of the Supercruise system, the majority of the data was collected during highway driving. Information collected includes vehicle CAN data as well as Lidar and camera data from a vehicle mounted sensor array. Vehicle CAN data and information on traffic surrounding the Ego-vehicle derived from the sensor array are postprocessed and merged to provide one combined CVS data file per drive. ![cadillac-ct6 image](ct6.png)

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Test Vector Development for Verification and Validation of Heavy-Duty Autonomous Vehicle Operations

The current focus in the ongoing development of autonomous driving systems (ADS) for heavy duty vehicles is that of vehicle operational safety. To this end, developers and researchers alike are working towards a complete understanding of the operating environments and conditions that autonomous vehicles are subject to during their mission. This understanding is critical to the testing and validation phases of the development of autonomous vehicles and allows for the identification of both the nominal and edge case scenarios encountered by these systems. Previous work by the authors saw the development of a comprehensive scenario generation framework to identify an operating domain specification (ODS), or external and internal conditions an autonomous driving system can expect to encounter on its mission to form critical scenario groups for autonomous vehicle testing and validating using statistical patterns, clustering, and correlation. Continuing this prior work, this paper focuses on the generation of test cases based on the critical scenarios identified that can be used to prioritize either the most common nominal driving scenarios or the least common severe driving scenarios. These test cases can then be used validate, through simulation or real-world testing, the operating design domain (ODD) for a generalized driving mission and built upon to identify spatial and temporal impacts on the driving mission of an autonomous vehicle.

Siekmann, Adam↗
DOE OSTI2023

Spatio-temporal and weather characterization of road loads of electrified heavy-duty commercial vehicles across U.S. interstate roads

Adoption of battery electric vehicles (BEV) in heavy duty (HD) commercial freight transportation is difficult due to technological and economic hurdles. Beyond safety and compliance, fleet and operational logistics necessitate both high uptime and parity with diesel system productivity/Total Cost of Ownership to support widespread deployment of electric powertrains. However, relatively high energy storage costs, along with the higher weight of BEV systems, limit the viability of HD commercial freight transport to shorter-range applications where smaller batteries will serve for mission energy requirements (single operational shift). Knowing the energy consumption and operating variations of these commercial vehicle systems is crucial for effectively sizing the energy storage systems. This paper is the first in a series of studies to understand the regional specific operating design domain variations of commercial Class 8 HD trucks and the associated impact to their energy requirements. In particular, the local weather conditions are shown to influence the total vehicle energy usage. Further, the impact of temperature, pressure, and humidity changes are shown to impact the local air density. This is needed to calculate aerodynamic drag on vehicles and has been found to play a significant role in the overall performance of a vehicle. Although the methodology of calculating air density varies only slightly throughout the literature, the application to transportation has still been limited. This study provides a means by which air density can be estimated for the contiguous U.S. using the NOAA MADIS dataset. These air density estimates were then used to determine vehicle performance in varying regions of the country to highlight the importance of consideration of weather variables when monitoring vehicle performance, but also to provide recommendations based on these locales upon fleet conversion to BEVs.

Moore, Amy↗
DOE OSTI2024

Integration of Safety, Security, and Safeguards During Design and Operations: A Technical Assessment and Regulatory Considerations for Advanced Reactor and Advanced Fuel Fabrication Facilities

This report presents the current state of knowledge, technology, methodologies, and tools that could be implemented to realize the robust integration of safety, security, and safeguards (3S) for advanced nuclear reactors (ARs) and advanced nuclear fuel cycle facilities. This report was motivated by the global development of ARs which are expected to play a key role in meeting domestic energy and climate objectives. Domestically, with many ARs in the early design phase, the integration of 3S provides an opportunity to achieve risk reduction while using less resources than traditional light water reactors by leveraging interdependencies and synergies between each domain. In addition, domestic policy considerations encourage the convergence of each 3S domain through facility design and operations. Therefore, there is a need to better understand the interdependencies and integration between 3S across ARs and advanced reactor fuel cycle facilities’ lifecycles including design, construction, and operational phases.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Decarbonization Dilemmas: Deliberating Difficult Decisions in Laboratory Design

Dive into the depths of design and decision-making, while we discuss the decarbonization of laboratory buildings! Delve into the dense domain of laboratory design and operations, where every development presents a diverse array of dilemmas and delights. Join us for these dynamic sessions focused on decoding the secrets of sustainable success. Dig deep into the dynamic world of heat pump designs and the delicate balance of heating and cooling loads. Debate between constant and variable fume hood designs, where these decisions determine outcomes. Discover the divergent paths of HVAC system implementation, from the deployment of chilled beams to the diverse array of different terminal unit types. But don't delay; decisive action is demanded for these goals! Dare to dream of decarbonization as we direct discussions on retrofitting existing building stock versus innovative new design approaches. Delve into the depths of debate and emerge with a decisive strategy for sustainable success. Discuss recent discoveries in development from experts associated with existing laboratory buildings with decarbonization goals. These insights and lessons learned will help determine the path forward in our industry's drive for decarbonization designs. Decarbonization is no easy task, but with determination, dedication, and devotion, we can defy the odds and forge a brighter future for laboratory design and operations. Let's dare to decarbonize together!

decarbonization↗
DOE OSTI2021

Fluid dynamic simulation and analysis of water-cooling systems for the Electron-Ion Collider

The Electron-Ion Collider is the newest large-scale project at Brookhaven National Laboratory. The collider’s purpose is to provide further advancements in the knowledge of the universe’s origin by accelerating particles near the speed of light. Our project for this 3.8 km ring was to create a thermal hydraulic steady-state simulation design of the water-cooling system to be cost-effective and energy efficient, as envisioned by Charlie Foltz, the EIC Infrastructure Division Director. The system would include a supply and return header, which cools several thousand components of the ring. The water would then be returned and cooled down using a system of cooling towers and plate and frame heat exchangers. Due to the size of the system and the complexity of the network analysis, a fluid dynamic simulation software, AFT Fathom, was used. Since previous methods of maintaining systems relied on building upon smaller real-life models and implementing empirical data, this flow model was unique and first of a kind in the domain of accelerator design, construction and operation. Therefore, our hydraulic team piloted a new method to perform network analysis on a large scale cooling system. We successfully created several test scenarios for system behavior in a shorter time compared to the method of performing hand calculations. Cooling specifications for heat rejection, pressure drop, flow rate, and pipe sizing were changed based on the individual systems of the vacuum, radio frequency (RF), magnet and power supply, and cryogenics sections. Finally, we used DOE guidelines to perform life-cycle cost analysis with net present value and carbon saving analysis on the systems where pipe size could be optimized.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

PDV Sensitivity [Slides]

The concept of PDV sensitive is not new. But we tend not to discuss sensitivity in a rigorous, quantified way. That should change. Indeed there is a natural definition, which is just the energy of light from the target within the analysis window that gives a SNR of 10 in the frequency domain. Systems designers, diagnostic engineers, and operators should use the language of sensitivity: (1) Absolute sensitivity: nW * ns; (2) Launch power: mW or dBm; (3) Relative surface return: dB; (4) Relative sensitivity: dB * ns. This will help us design better systems, choose the correct system for the job, maintain our systems, and tune our systems.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Opportunities for retrieval and tool augmented large language models in scientific facilities

Upgrades to advanced scientific user facilities such as next-generation x-ray light sources, nanoscience centers, and neutron facilities are revolutionizing our understanding of materials across the spectrum of the physical sciences, from life sciences to microelectronics. However, these facility and instrument upgrades come with a significant increase in complexity. Driven by more exacting scientific needs, instruments and experiments become more intricate each year. This increased operational complexity makes it ever more challenging for domain scientists to design experiments that effectively leverage the capabilities of and operate on these advanced instruments. Large language models (LLMs) can perform complex information retrieval, assist in knowledge-intensive tasks across applications, and provide guidance on tool usage. Using x-ray light sources, leadership computing, and nanoscience centers as representative examples, we describe preliminary experiments with a Context-Aware Language Model for Science (CALMS) to assist scientists with instrument operations and complex experimentation. With the ability to retrieve relevant information from facility documentation, CALMS can answer simple questions on scientific capabilities and other operational procedures. With the ability to interface with software tools and experimental hardware, CALMS can conversationally operate scientific instruments. By making information more accessible and acting on user needs, LLMs could expand and diversify scientific facilities’ users and accelerate scientific output.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Artificial Intelligence/Machine Learning Technologies for Advanced Reactors (Workshop Summary Report)

A workshop on artificial intelligence and machine learning (AI/ML) for advanced reactors (AR) was held October 5-6, 2021. The workshop was to be attended in-person at ANL but COVID restrictions forced the workshop to go virtual. The objectives of the workshop were to identify the most promising AI/ML opportunities for improving advanced reactor design, optimizing plant performance, and enhancing economic competitiveness and to develop an understanding of the scientific, engineering and licensing challenges facing their application. The workshop planning committee included GAIN, EPRI and NEI and members of three national laboratories (ANL, INL, and ORNL). The workshop was attended by more than 200 individuals representing academic and scientific institutions and the nuclear power industry. The definition put forth for an AI/ML system was one that perceives its environment and takes actions that maximize its chance of achieving its goals. In this report AI/ML refers to next generation algorithms that include deep learning, statistical analysis and data analytics and associated scientific computing and their potential application to the design, licensing, operation and maintenance of ARs. These methods typically incorporate models built from process data and may also include data generated by simulations that represent the behavior of a system. The workshop was organized in response to the growing interest in application of AI/ML for improving the economic competitiveness of nuclear energy. Increasingly more resources are being allocated to investigating the benefits of AI/ML methods. The DOE created the Artificial Intelligence & Technology Office to promote their development. And within the Office of Nuclear Energy, resources have been allocated to explore and understand the potential benefits of AI/ML. Additionally, the national laboratories are strategically positioned with DOE computing facilities such as Summit, Perlmutter, Aurora and Frontier that support large-scale simulations, hybrid HPC models with AI surrogates, and the exploration of new types of generative models emerging from multi-model data streams and sources. The workshop was organized with members of the AR community to understand the effort and to identify the level of interest and progress in this emerging technology. The workshop discussions focused on identifying opportunities for AI/ML across diverse areas of the nuclear industry and identifying current scientific and engineering challenges for advanced reactors that might be addressed through transformational uses of AI/ML. Discussion panels focused on four high-interest technical domains for advanced reactors: design, maintenance and operations, energy storage, and materials. The results of those discussions are summarized in this report. This includes opportunities that were identified for exploiting AI techniques and methods to improve the efficacy and efficiency of reactor analysis and to improve the operation and optimization of advanced reactors. Advanced reactor developers expressed an interest in learning more about AI/ML methods and their application. This included understanding whether ML methods can provide an advantage over existing nonlinear data regression methods for collapsing high-fidelity simulation results into faster running models. A consensus emerged that AR advances planned for the next decade will benefit from the use of AI/ML tools. The need exists to understand and model complex systems across length scales and modalities. AI/ML is a tool for discovery that can yield a set of engineering principles for use by nuclear engineers, licensing bodies, and operators to solve problems in plant design, safety analyses, autonomous operation, and predictive maintenance. While AI/ML represents a new set of tools, an awareness by the nuclear community of the full potential is still in the early stages so there is a need to increase awareness. It appears that the wide-spread adoption of AI/ML tools for ARs would be facilitated by future educational workshops that describe foundational methods and capabilities and describe successful applications.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

Achieving Diesel Powertrain Ownership Parity in Battery Electric Heavy Duty Commercial Vehicles Using a Rapid Recurrent Recharging Architecture

Battery electric vehicles (BEV) in heavy duty (HD) commercial freight transport face challenging technoeconomic barriers to adoption. Specifically, beyond safety and compliance, fleet and operational logistics require both high up-time and parity with diesel system productivity/Total Cost of Ownership (TCO) to enable strong adoption of electrified powertrains. At present, relatively high energy storage prices coupled with the increased weight of BEV systems limit the practicality of HD commercial freight transport to shorter range applications, where smaller batteries will suffice for the mission energy requirements (single operational shift). This paper presents an approach to extend the feasibility of BEV HD trucking for a broad range of applications. The concept is based on the leveraging rapid and recurrent recharging of a BEV HD truck that may either already make frequent stops due to shipment drop-offs/reloading or be required to make frequent stops along with longer missions for recharging. While the challenges of the latter are well appreciated, the concept proposed explores making minimal impact to overall mission time by targeting high C-rate charging while optimizing the frequency (miles) through which these events must occur. The concept optimizes battery size and chemistry, such that the expected life (years and total energy throughput) is balanced with the depth of discharge between recharging events, thus making complete use of the energy available through the life of the battery system. The solution is constrained to minimize the impact on payload capacity. The paper analyzes critical levers in achieving diesel price parity (based on a simplified vehicle TCO), achieved through different purchase options (including lease versus buy) and operational models (half-life swap out). Finally, the paper identifies the application design domain where these solutions are viable with limited impact on fleet operations.

Sujan, Vivek↗
DOE OSTI2024

Designing workflows for materials characterization

Experimental science is enabled by the combination of synthesis, imaging, and functional characterization organized into evolving discovery loop. Synthesis of new material is typically followed by a set of characterization steps aiming to provide feedback for optimization or discover fundamental mechanisms. However, the sequence of synthesis and characterization methods and their interpretation, or research workflow, has traditionally been driven by human intuition and is highly domain specific. Here, we explore concepts of scientific workflows that emerge at the interface between theory, characterization, and imaging. In this study, we discuss the criteria by which these workflows can be constructed for special cases of multiresolution structural imaging and functional characterization, as a part of more general material synthesis workflows. Some considerations for theory–experiment workflows are provided. We further pose that the emergence of user facilities and cloud labs disrupts the classical progression from ideation, orchestration, and execution stages of workflow development. To accelerate this transition, we propose the framework for workflow design, including universal hyperlanguages describing laboratory operation, ontological domain matching, reward functions and their integration between domains, and policy development for workflow optimization. These tools will enable knowledge-based workflow optimization; enable lateral instrumental networks, sequential and parallel orchestration of characterization between dissimilar facilities; and empower distributed research.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Fuel Flow Modeling under Thermal Fluidics Considerations within Operational Domains of Molten Salt Reactors

Molten Salt Reactors (MSRs) offer significant versatility supported by their design flexibility and safety advantages due to high temperatures, design customization capabilities, strong negative reactivity feedback effects, and salt chemistry characteristics. Different control schedules and different mass flow rates in the MSR primary systems impact neutronics and thermal hydraulics coupling. The result is two-fold. First, it leads to adaptability in various applications when the customization of a MSR unit is desired. Second, it calls for the quantification of dynamics and safety characteristics to assess the operational domain and make sure reactor stability and inherent safety characteristics are maintained. This paper provides an in-depth transient analysis of the operational implications of MSRs having heat-generating fuel and heat-transporting salt mixed forming liquid fuel salts contained within primary systems. A coupling model was developed to simulate fuel flow in molten salt reactors. A zero-dimensional reactor kinetics model was used with a one-dimensional heat transfer model in the reactor core. The temperature reactivity feedbacks of the fuel salt resulting from the thermal-hydraulics model were used in the reactor kinetics model to complete the coupled code. The model was applied to simulate the Molten Salt Breeder Reactor (MSBR) response at steady state and transients. The developed model was shown to be a suitable tool for the dynamic analysis of molten salt reactors.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Spread Spectrum Time Domain Reflectometry (SSTDR) and Frequency Domain Reflectometry (FDR) for Detection of Cable Anomalies Using Machine Learning

Cables are initially qualified for nuclear power plant use for 40 years. As plants extend their operating license to 60 and 80 years, continued use of these cables must shift to a performance-based approach since it is cost prohibitive to completely replace cables that are likely still capable of performing their design function. A variety of cable tests are available and are commonly applied during outages when the cables can be taken out of service. Frequency domain reflectometry (FDR) is one of these test methods that is being more broadly accepted and used because it not only detects anomalies along the cable with a low-voltage signal that does not stress the cable insulation, but the technique also locates the anomalies. This supports follow-up local inspection and local repair or partial replacement of a damaged cable segment. Currently, FDR testing is only applied to cables that are taken out of service since the test instrument would be damaged by operational voltages. A related technology that has found some acceptance in the aircraft and rail industry is spread spectrum time domain reflectometry (SSTDR). This technology has been implemented with a custom commercial instrument by LiveWire Innovation Inc. that is designed to operate on live cables up to 1000 volts. One of the main conclusions of a previous effort was that cable reflectometry plots can be difficult for humans to analyze due to baseline noise, low or noisy anomaly response peaks, or large responses from cable ends. Detection of cable anomalies for many of these frequencies and test conditions was challenging for manual analysis. This presented an ideal opportunity for ML analysis to distinguish undamaged cable indications from anomalous cable indications. This research discusses application of machine learning (ML) to reflectometry cable test methods. The goal was to assess feasibility to distinguish undamaged cable reflectometry responses from damaged or anomalous cable reflectometry responses. The assessment considered the 3 instruments, multiple frequency bandwidths from each instrument, multiple cable anomalies and test conditions, and both supervised and unsupervised ML approaches. Although approaches and analysis methods were not identical or directly comparable, both outputs were encouraging. The unsupervised prediction weighted accuracy was assessed by instrument and by frequency. It performed better at high frequencies with the highest prediction accuracy of 0.84 for the higher frequency FDR, 0.79 for the 48-MHz LiveWire SSTDR, and 0.77 for 300-MHz PNNL SSTDR. The initial weighted accuracy average across all frequencies for using supervised ML was 0.56 to 0.68. The supervised analysis was repeated with noisier training data removed resulting in weighted accuracies of 0.69 to 0.87. These weighted accuracies are not directly comparable due to differences in the supervised and unsupervised analysis details but do indicate an encouraging trend. Even with limited and unbalanced data, strong prediction accuracies seem encouraging for further work including more data under a wider range of conditions.

42 ENGINEERING↗
DOE OSTI2021

Inverse Design of Plasma Metamaterial Devices for Optical Computing

We apply inverse-design methods to produce two-dimensional plasma metamaterial (PMM) devices. Backpropagated finite-difference frequency-domain (FDFD) simulations are used to design waveguides and demultiplexers operating under both TE and TM modes. Demultiplexing and waveguiding are demonstrated for devices composed of plasma elements with reasonable plasma densities of approximately 7 GHz, allowing for future in situ training and experimental realization of these designs. We also explore the possible applicability of PMMs to nonlinear boolean operations for use in optical computing. Functionally complete logical connectives (or and and) are achieved in the TM mode.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

SSTDR and FDR Detection of Un-Energized and Energized Cable Anomalies Including Thermal Degradation Using Machine Learning

Historically, cables are initially qualified for nuclear power plant use for 40 years. As plants extend their operating license to 60 and 80 years, continued use of these cables must shift to a performance-based approach since it is cost prohibitive to completely replace cables that are likely still capable of performing their design function. A variety of cable tests are available and are commonly applied during outages when the cables can be taken out of service. Frequency domain reflectometry (FDR) is one of these test methods that is being more broadly accepted and used because it not only detects anomalies along the cable with a low-voltage signal that does not stress the cable insulation, but the technique also locates the anomalies. This supports follow-up local inspection and local repair or partial replacement of a damaged cable segment. Currently, FDR testing is only applied to cables that are taken out of service since the test instrument would be damaged by operational voltages. A related technology that has found some acceptance in the aircraft and rail industry is spread spectrum time domain reflectometry (SSTDR). This technology has been implemented with a custom commercial instrument by LiveWire Innovation that is designed to operate on live cables up to 1000 volts and with a bandwidth of 48 MHz. Initial evaluation by the Pacific Northwest National Laboratory (PNNL) of the Live Wire system indicated that a broader bandwidth (BW) SSTDR may be better for many kinds of flaws. This led PNNL to develop an SSTDR laboratory instrument suitable for tests up to 500 MHz bandwidth. Testing on energized cables is also desirable for online monitoring systems so an inductive clamshell coupler was developed that allows energized cables to be tested up to at least 5 kV and likely higher voltage levels. Dielectric spectroscopy and tan delta testing plus various laboratory destructive tests were included in this data acquisition campaign directed to feed a machine learning (ML) study. With these kinds of developments, online energized cable tests may be possible with industrial adoption of such hardware advances but it will be completely impractical to have highly skilled data analysts continually examine these complex signals for indications of damage or compromised conditions. If online testing is to be implemented in new test hardware, it must be accompanied by software that can interpret the signals and alert plant operators of changing or degraded conditions. The thermally aged, shielded cable investigated here was separately treated for ML analysis. Visual analysis of electrical data showed generally increasing peaks where the cable entered and exited the oven. These peaks were not exactly aligned with expected locations, but these differences were attributed to velocity of propagation calibration errors. Only supervised ML was applied to the thermally aged data as this data was only available shortly before the committed publication date of this report. The supervised ML was structured to divide the 0 to 70-day responses as ‘normal’ from 0 to 35 days or ‘anomalous’ from 36 to 70 days, based on cable tensile elongation at break (EAB) insulation characterization. Using 80% of the data for training and 20% for testing, the supervised ML predicted normal versus anomalous was 70% accurate. Important conclusions include: • Accuracy to predict the presence of cable damage is improved from the 2023 effort by more training data. Weighted accuracies for comparisons among the instruments ranged from 67 to 89 % for unsupervised ML and 71 to 99% for supervised ML. • Based on the synthetic data tests, the unsupervised models are more generalizable to unseen anomalies. The Multi-Layer Perceptron classifier (MLP) model reported as high as 99.7% accuracy on the test data, but this dropped to 58.3% when tested on the synthetic data. In contrast, the unsupervised Pointwise model only achieved 89.7% accuracy on the experimental data but reported 78.3% accuracy on the synthetic data. • The best anomaly indicators are higher frequency (400 MHz BW) FDR data. Other tests may be interesting but for this study, this was the best predicter.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

AURORA: Automated Refinement of Coarse-Grained Reconfigurable Accelerators

Coarse-grained reconfigurable arrays (CGRAs), loosely defined as arrays of functional units interconnected through a network-on-chip (NoC), provide higher flexibility than domain-specific ASIC accelerators while offering increased hardware efficiency with respect to fine-grained reconfigurable devices, such as Field Programmable Gate Arrays (FPGAs). Un-fortunately, designing a CGRA for a specific application domain involves enormous software/hardware engineering effort (e.g., designing the CGRA, map operations onto the CGRA, etc) and requires the exploration on a large design space (e.g., applying appropriate loop transformation on each application, specializing the reconfigurable processing elements of the CGRA, refining the network topology, deciding the size of the data memory, etc). Int his paper, we propose AURORA – a software/hardware co-design framework to automatically synthesize optimal CGRA given a set of applications of interest

Tan, Cheng↗
DOE OSTI2023

Orca2

SAND2024-03236O Orca2 is a trajectory design and optimization code for constructing and solving problems numerically defined on homogeneous manifolds. Orca2 solves numerical trajectory design and optimization problems defined on Lie-type domains and operates primarily as a library requiring a user to write additional C++ code to leverage it. 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.

Sparapany, Michael↗
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↗