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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 OSTI2025

SLIA Reference Architecture Models

The SLIA Reference Architecture Models project, sponsored by the DOE CESER Energy CyberSense Program (Oct 2024–Sep 2025), advanced LLNL’s PySCES simulation tool to better support CyTRICS Prioritization and Initial Risk Assessment (PIRA) reference architectures. Key achievements include enhancements to the PySCES transmission substation facility model, expanded asset coverage, and enhancements to the PySCES code base. Software improvements reduced code complexity, migrated PySCES to Python version 3.11, introduced an object-oriented design, and added a schema database for easier updates and validation. New features support device criticality assessments and a more precise parametric simulation mode. Remaining gaps include model validation, workflow limitations, Monte Carlo convergence issues, full device criticality metric implementation, model fidelity, and general software improvements. Continued development is recommended to address these gaps and fully align PySCES with CyTRICS PIRA requirements.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

5G Communications in Nuclear: Potential Use Cases and Security Considerations

As fifth-generation (5G) communications continues to revolutionize the future of wireless technology, there is growing demand to utilize its benefits for critical infrastructures such as nuclear power plants (NPPs). In regard to achieving full automation and control in the operation of existing and future nuclear reactors, the unique capabilities of 5G can bring several potential advantages over other wireless technologies. However, a deep investigation is needed for the availability and security of 5G communications under various NPP operational scenarios. This article examines how 5G security capabilities can be architecturally deployed in nuclear applications so as to replace existing communication infrastructures. We discuss the current use of all wireless technologies in NPPs with their key features. Consequently, we investigated several NPP use cases in which 5G offers potential advantages but entails specific security considerations. The present article covers the characteristics of 5G communications, general challenges to its application in nuclear, and the security gaps that need to be addressed. We also highlight certain 5G security-by-design features that can help addressing current stringent NPP requirements. In addition, we discuss some future research direction that can facilitate the implementation of 5G in a nuclear facility. The findings presented herein can help foster 5G deployment in NPPs, thus enabling secured data transmission, cost savings, and increased operational efficiency with enhanced reliability.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2020

Measuring the dynamic response of the thylakoid architecture in plant leaves by electron microscopy

The performance of the photosynthesis machinery in plants, including light harvesting, electron transport, and protein repair is controlled by structural changes of the thylakoid membrane system inside the chloroplasts. In particular, the structure of the stacked grana area of thylakoid membranes is highly dynamic, changing in response to different environmental cues such as light intensity. For example, the aqueous thylakoid lumen enclosed by thylakoid membranes in grana has been documented to swell in the presence of light. However, light-induced alteration of the stromal gap in the stacked grana (partition gap) and of the unstacked stroma lamellae have not been well characterized. Light-induced changes of the entire thylakoid membrane system including the lumen in both stacked and unstacked domains as well as the partition gap are presented here, and the functional implications are discussed. This structural analysis was made possible by development of a robust semi-automated image analysis method combined with optimized plant tissue fixation techniques for transmission electron microscopy generating quantitative structural results for the analysis of thylakoid ultrastructure.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Federated Learning with Frequency Estimation for Smart Meter Systems

Federated learning (FL) is a powerful framework that enables multiple distributed clients to collaborate without the need to transfer their data to a central server. However, FL does not inherently guarantee the level of privacy that clients often require. In our review of recent studies on privacy-enhancing techniques in FL, we found that frequency estimation (FE) methods remain underexplored. To address this gap, we developed and integrated FE techniques on the client side, further examining the effects of incorporating an adaptive range and a shuffled model. We also analyzed the impact of varying hyper-parameters on privacy preservation. Our results provide clear guidance on the algorithms and configurations that are most effective for enhancing privacy in FL, particularly when using long short-term memory (LSTM) architectures.

Kotevska, Olivera [ORNL] (ORCID:0000000316772243)↗
DOE OSTI2024

Flow field design for zero-gap microbial electrolysis cells using synthetic and real wastewater

Increasing performance in microbial electrolysis cells (MECs) requires the development of optimized reactor configurations with minimal internal resistance and capable to operate with real wastewater. Here, the impact of two different flow fields (serpentine and circular) was examined in zero-gap MECs with synthetic and real wastewaters. The serpentine flow field enabled a uniform distribution of the electrolyte in the anode chamber, resulting in larger current densities at lower flow rates compared to the circular flow field. Electrochemical tests using synthetic media with high buffer capacity revealed more stable and higher performance with the serpentine flow field compared to the circular flow path, producing larger current density (23.7 ± 0.8 A/m 2 vs 21.9 ± 5.6 A/m 2 ), hydrogen production rate (75.8 ± 4.1 L/L-d vs 54.3 ± 2.4 L/L-d), cathodic coulombic efficiency (>91 % vs >50 %), and an overall lower internal resistance (12.5 ± 0.5 mΩm 2 vs 14.8 ± 3.7 mΩm 2 ). Continuous operation for over 30 days with real wastewater indicated higher tolerance of the MECs with serpentine flow field toward media with large concentration of suspended solids, producing a current density of 5.4 ± 1.1 A/m 2 and a hydrogen production rate of 22.2 ± 6.2 L/L-d. Furthermore, the results presented here underscore the importance of reactor design and architecture in optimizing MEC performance for hydrogen production from liquid wastes.

flow path↗
DOE OSTI2025

Integrating Safety, Security, and Nuclear Operations for Advanced Reactors

The traditional separation between safety, security, and operations teams has created significant barriers to achieving optimal outcomes. When security considerations are introduced late in the design process, they often conflict with already-established architectural, operational, or engineering parameters. Retrofitting security measures can lead to increased costs, schedule delays, and compromises in security effectiveness. For instance, the need to retrofit physical barriers or surveillance systems often results in trade-offs that could have been avoided with earlier input from security professionals. Delayed integration can also affect regulatory processes and result in licensing delays. Security reviews conducted at later stages frequently identify gaps that necessitate significant redesign efforts, impacting not only scope, schedule, and budget, but also adding risk and lowering stakeholder confidence in the project. This paper aims to address these challenges by identifying practical opportunities for integrating security considerations seamlessly with design and operations teams throughout the entire lifecycle of nuclear facilities—from conceptual design to commissioning and beyond. The research emphasizes the value of early and continuous collaboration among stakeholders to ensure that security measures are robust, operationally effective, and cost-efficient. By examining case studies, analyzing past incidents, and leveraging best practices from other high-security industries, this study highlights actionable strategies for bridging the gap between safety, security, and operations teams.

Zineddin, Dr. Z. [ORNL] (ORCID:0009000848740725)↗
DOE OSTI2019

Biochemical and structural analyses reveal that the tumor suppressor neurofibromin (NF1) forms a high-affinity dimer

Neurofibromin is a tumor suppressor encoded by the NF1 gene, which is mutated in Rasopathy disease neurofibromatosis type I. Defects in NF1 lead to aberrant signaling through the RAS–mitogen-activated protein kinase pathway due to disruption of the neurofibromin GTPase-activating function on RAS family small GTPases. Very little is known about the function of most of the neurofibromin protein; to date, biochemical and structural data exist only for its GAP domain and a region containing a Sec-PH motif. To better understand the role of this large protein, here we carried out a series of biochemical and biophysical experiments, including size-exclusion chromatography–multiangle light scattering (SEC-MALS), small-angle X-ray and neutron scattering, and analytical ultracentrifugation, indicating that full-length neurofibromin forms a high-affinity dimer. We observed that neurofibromin dimerization also occurs in human cells and likely has biological and clinical implications. Analysis of purified full-length and truncated neurofibromin variants by negative-stain EM revealed the overall architecture of the dimer and predicted the potential interactions that contribute to the dimer interface. We could reconstitute structures resembling high-affinity full-length dimers by mixing N- and C-terminal protein domains in vitro. The reconstituted neurofibromin was capable of GTPase activation in vitro, and co-expression of the two domains in human cells effectively recapitulated the activity of full-length neurofibromin. Taken together, these results suggest how neurofibromin dimers might form and be stabilized within the cell.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Mind-the-gap - Part III: Doppler velocity measurements from space

Convective motions and hydrometeors microphysical properties are highly sought-after parameters for evaluating atmospheric numerical models. With most of the Earth's surface covered by water, space-borne Doppler radars are ideal for acquiring such measurements at a global scale. While these systems have proven to be useful tools for retrieving cloud microphysical and dynamical properties from the ground, their adequacy, and specific requirements for spaceborne operation still need to be evaluated. Comprehensive forward simulations enable us to assess the advantages and drawbacks of six different Doppler radar architectures currently planned or under consideration by space agencies for the study of cloud dynamics. Radar performance is examined against state-of-the-art numerical model simulations of well-characterized shallow and deep, continental, and oceanic convective cases. Mean Doppler velocity (MDV) measurements collected at multiple frequencies (13, 35 and 94 GHz) provide complementary information in deep convective cloud systems. The high penetration capability of the 13-GHz radar enables to obtain a complete, albeit horizontally under-sampled, view of deep convective storms. The smaller instantaneous field of view (IFOV) of the 35-GHz radar captures more precise information about the location and size of convective updrafts above 5-8 km height of most systems which was determined is the portion of storms where the mass flux peak is typically located. Finally, the lower mean Doppler velocity uncertainty of displaced phase center antenna (DPCA) radars makes them an ideal system for studying microphysics in shallow convection and frontal systems, as well as ice and mixed-phase clouds. It is demonstrated that a 94-GHz DCPA system can achieve retrieval errors as low as 0.05-0.15 mm for raindrop volume-weighted mean diameter and 25% for rime fraction (for a -10 dBZ echo).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

A distributed voltage inference framework for cyber-physical attacks detection and localization in active distribution grids

The transition to active distribution grids with real-time monitoring and control depends on the proliferation of advanced communication networks and devices. This paradigm shift towards a cyber-physical architecture also introduces new vulnerabilities for adversaries to exploit and launch sophisticated cyber-physical attacks targeting grid observability. Current research highlights the challenges in distinguishing attacks on voltage phasor or nodal injection measurements and isolating multi-source attack locations in a multiphase distribution grid. The attack detection and localization methods in literature face accuracy issues, applications across diverse attack scenarios, or scalability limits. Here, to bridge these gaps, this paper proposes a distributed Voltage Inference framework for real-time detection and localization of cyber-physical attacks, addressing scalability, adaptability, and accuracy challenges in state-of-the-art methods. The proposed methodology leverages the distributed nature of the Voltage Inference framework through a two-step process of prediction and correction, together with a tractable graph partitioning approach, providing a reliable solution to identify compromised measurement sources and facilitate isolation. Extensive testing on IEEE 13 and 123-node distribution feeders underscores the algorithm’s efficacy, enhancing the security and resilience of active distribution grids against evolving cyber threats. Additionally, Hardware-in-the-Loop (HIL) implementation validates the proposed strategy’s practical applicability in real-world scenarios.

active distribution grids↗
DOE OSTI2021

Demystifying asynchronous I/O Interference in HPC applications

With increasing complexity of HPC workflows, data management services need to perform expensive I/O operations asynchronously in the background, aiming to overlap the I/O with the application runtime. However, this may cause interference due to competition for resources: CPU, memory/network bandwidth. The advent of multi-core architectures has exacerbated this problem, as many I/O operations are issued concurrently, thereby competing not only with the application but also among themselves. Furthermore, the interference patterns can dynamically change as a response to variations in application behavior and I/O subsystems (e.g. multiple users sharing a parallel file system). Without a thorough understanding, I/O operations may perform suboptimally, potentially even worse than in the blocking case. To fill this gap, here we investigate the causes and consequences of interference due to asynchronous I/O on HPC systems. Specifically, we focus on multi-core CPUs and memory bandwidth, isolating the interference due to each resource. Then, we perform an in-depth study to explain the interplay and contention in a variety of resource sharing scenarios such as varying priority and number of background I/O threads and different I/O strategies: sendfile, read/write, mmap/write underlining trade-offs. The insights from this study are important both to enable guided optimizations of existing background I/O, as well as to open new opportunities to design advanced asynchronous I/O strategies.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

PyHydroGeophysX: An extensible open-source platform for integrating hydrological models with geophysical measurements

Hydrological models and geophysical measurements are widely used tools for understanding subsurface hydrological processes relevant to water resource management, yet they typically remain disconnected due to technical barriers. We present PyHydroGeophysX, an open-source Python platform bridging this gap by providing standardized interfaces between hydrological modeling software (MODFLOW, ParFlow) and geophysical simulation tools (PyGIMLi, SimPEG). The platform implements bidirectional workflows: translating hydrological outputs into simulated geophysical responses through petrophysical models, and extracting hydrological information from geophysical inversions. Key features include bidirectional workflow modules, configurable petrophysical models, time-lapse inversion with temporal regularization, parallel computing, and mesh utilities for property transfer between geophysical and hydrological grids. The modular architecture of PyHydroGeophysX enables researchers to incorporate additional models and methods, fostering broader adoption of integrated hydrogeophysical approaches. The software is freely available on GitHub and is intended for researchers and practitioners working at the intersection of hydrology and geophysics.

Hydrogeophysics↗
DOE OSTI2020

Challenges for and Pathways Toward Solid-State Batteries

Solid-state batteries utilizing lithium metal anodes have the potential to enable batteries with a specific energy of >500 Wh/kg and an energy density of >1,500 Wh/L for thousands of cycles. When optimized they will improve the energy efficiency, operating temperature range, sustainability, and safety at a lower cost compared to projections for advanced Li-ion batteries. This improved performance is critical for the widespread adoption of electric vehicles and may enable future applications such as electric aviation. Expectations for solid-state batteries are high, there are significant materials and processing challenges that need to be overcome. Some of these challenges are well known; others are more subtle and are just becoming known. The challenges and their solutions must be clearly identified to realize high-energy solid-state Li metal batteries. In the United States, the U.S. Department of Energy (DOE) funds the majority of energy storage research and development, including solid state battery research. Web of Science publication analysis shows that worldwide DOE is second largest funder. Work on solid state batteries is distributed across different arms of DOE and is coordinated at headquarters to ensure a broad national portfolio for advanced batteries, including lithium batteries and solid-state battery devices. We note that university and national laboratory researchers in the US with a long-standing interest in solid state batteries is a small, integrated community with recognized impact for publications, patents and startup companies. The opportunity for this group to engage for research with international colleagues is growing, fostered by periodic bilateral meetings supported by DOE and by the recent joint US German research program for lithium battery interface studies. These are valuable opportunities to speed solid-state battery development. The ORNL led virtual workshop, overviewed here, confirms the community’s shared vision of the exciting advances, opportunities, and challenges. Further, we are seeing that this workshop has spawned several new informal collaborations and draft proposals. This encourages us to propose future discussion. On May 11, 2020, Oak Ridge National Laboratory (ORNL) hosted a 6-hour, on-line national workshop to discuss recent advances and most the prominent obstacles to realizing solid-state Li metal batteries. The workshop included more than 30+ experts from national laboratories, universities, and companies, all of whom have worked on solid-state batteries for multiple years. The participants shared recent advances, many not yet in print, illustrating that the community has gained significant new insights for solid-state Li battery materials over the last 5 years. In this report, the major outcomes of the workshop are organized to identify the gaps in our scientific knowledge for four core materials science areas: (1) Li metal anodes, (2) the solid electrolyte in contact with Li metal, (3) active cathode materials and solid-state composite cathodes, and (4) solid electrolytes. Illustrative examples and discussions are reported with the more comprehensive issues in the report. Discussion of additional challenges related to processing of solid-state battery materials and to the designs and architectures for mechanically robust, long-lived batteries received less attention due to time constraints. These are good themes for deeper discussion at a follow-on workshop, where a complete list of critical research topics can be identified.

25 ENERGY STORAGE↗
DOE OSTI2026

Heavy-Duty Nonroad Material Handler Electrification Part 1: Real-World Drive Cycle Development

Knowing a detailed operating cycle is critical for developing and testing equipment. Operating cycles can be separated by two clear distinctions: (1) regulatory or non-regulatory and (2) application at the engine-only or full machine level. The Environmental Protection Agency’s (EPA) Nonroad Transient Cycle (NRTC) may be a good representation of engine use in many types of equipment, but there is a gap in standardized and validated drive cycles specifically for nonroad material handlers. Lacking a standardized drive cycle makes it difficult to accurately benchmark machine performance and validate new powertrain technologies. The objective of this investigation is to illustrate the development of a custom drive cycle augmented with real-world customer use data that serves multiple purposes: (1) understand the range of operation and utilization that formulated inputs for electrified architecture analysis and (2) develop a repetitive and consistent maneuver to establish baseline energy consumption enabling equivalent comparison to future electrified prototype builds. This article presents a solution specifically for a 23-ton nonroad material handler in which material handling, machine transport, and extended idle were homologated to form representative short cycles defined by machine velocity and hydraulic cylinder position. The most intensive material handling short cycles had a load factor of 40% and an average fuel rate of 16 L/h. Combined with a visual aid, the short cycles exhibited low variability, having less than 5% root mean square (RMS) error in lift and reach position with respect to the average. The machine’s performance on these short cycles at the Advanced Power Systems Research Center (APSRC) was compared to results from two real-world customer locations operating the instrumented test machine in a cyclical manner, and for similar ground conditions were found to be comparable in fuel consumption.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Scaling Laws of Graph Neural Networks for Atomistic Materials Modeling

Atomistic materials modeling is a critical task with wide-ranging applications, from drug discovery to materials science, where accurate predictions of the target material property can lead to significant advancements in scientific discovery. Graph Neural Networks (GNNs) represent the state-of-the-art approach for modeling atomistic material data thanks to their capacity to capture complex relational structures. While machine learning performance has historically improved with larger models and datasets, GNNs for atomistic materials modeling remain relatively small compared to large language models (LLMs), which leverage billions of parameters and terabyte-scale datasets to achieve remarkable performance in their respective domains. To address this gap, we explore the scaling limits of GNNs for atomistic materials modeling by developing a foundational model with billions of parameters, trained on extensive datasets in terabytescale. Our approach incorporates techniques from LLM libraries to efficiently manage large-scale data and models, enabling both effective training and deployment of these large-scale GNN models. This work addresses three fundamental questions in scaling GNNs: the potential for scaling GNN model architectures, the effect of dataset size on model accuracy, and the applicability of LLM-inspired techniques to GNN architectures. Specifically, the outcomes of this study include (1) insights into the scaling laws for GNNs, highlighting the relationship between model size, dataset volume, and accuracy, (2) a foundational GNN model optimized for atomistic materials modeling, and (3) a GNN codebase enhanced with advanced LLM-based training techniques. Our findings lay the groundwork for large-scale GNNs with billions of parameters and terabyte-scale datasets, establishing a scalable pathway for future advancements in atomistic materials modeling.

Li, Chaojian [ORNL] (ORCID:0000000340309777)↗
DOE OSTI2022

A computational pipeline to generate a synthetic dataset of metal ion sorption to oxides for AI/ML exploration

The charged mineral/electrolyte interfaces are ubiquitous in the surface and subsurface–including the surroundings of the geological disposal sites for radioactive waste. Therefore, understanding how ions interact with charged surfaces is critically important for predicting radionuclide mobility in the case of waste leakage. At present, the Surface Complexation Models (SCMs) are the most successful thermodynamic frameworks to describe ion retention by mineral surfaces. SCMs are interfacial speciation models that account for the effect of the electric field generated by charged surfaces on sorption equilibria. These models have been successfully used to analyze and interpret a broad range of experimental observations including potentiometric and electrokinetic titrations or spectroscopy. Unfortunately, many of the current procedures to solve and fit SCM to experimental data are not optimal, which leads to a non-transferable or non-unique description of interfacial electrostatics and consequently of the strength and extent of ion retention by mineral surfaces. Recent developments in Artificial Intelligence (AI) offer a new avenue to replace SCM solvers and fitting algorithms with trained AI surrogates. Unfortunately, there is a lack of a standardized dataset covering a wide range of SCM parameter values available for AI exploration and training–a gap filled by this study. Here, we described the computational pipeline to generate synthetic SCM data and discussed approaches to transform this dataset into AI-learnable input. First, we used this pipeline to generate a synthetic dataset of electrostatic properties for a broad range of the prototypical oxide/electrolyte interfaces. The next step is to extend this dataset to include complex radionuclide sorption and complexation, and finally, to provide trained AI architectures able to infer SCMs parameter values rapidly from experimental data. Here, we illustrated the AI-surrogate development using the ensemble learning algorithms, such as Random Forest and Gradient Boosting. These surrogate models allow a rapid prediction of the SCM model parameters, do not rely on an initial guess, and guarantee convergence in all cases.

Li, Chunhui↗
DOE OSTI2021

Multiscale Characterization of the Caney Shale — An Emerging Play in Oklahoma

From a hydrocarbon perspective, the Caney Shale has historically been evaluated as a sealing unit, which resulted in limited studies characterizing the rock properties of the Caney Shale and its suitability for hydraulic fracturing. The objective of our research is to help bridge the current knowledge gap through the integration of multiscale laboratory techniques and to characterize the macro- and microscale rock properties of the Caney Shale. We employed an integrated approach for the characterization of the Caney using 200 ft (61 m) of Caney core from a target well in southern Oklahoma. Core observation and petrographic analysis of thin sections were combined to characterize the general rock types and associated fabrics and textures. Mineralogical composition, pore system architecture, and rock fabric were analyzed using x-ray diffraction (XRD), scanning electron microscopy/energy dispersive x-ray spectroscopy (SEM/EDS), and focused ion beam (FIB)-SEM. In addition, rebound hardness and indentation testing were carried out to determine rock hardness (brittleness) and elasticity, respectively. With the integrated multiscale characterization, three mixed carbonate-siliciclastic rock types were identified — mudstone, calcareous siltstone, and silty carbonate — likely representing a spectrum of deposition from low to relatively high energy environments in the distal portions of a ramp system. Silty carbonate contains mostly interparticle pores. The calcareous siltstones and silty mudstones contain a combination of organic matter pores and interparticle pores. Each of the rock types shows unique mineralogical compositions based on XRD. The mudstone lithofacies has the highest clay content and the least carbonate content. Calcareous siltstones show moderate carbonate and clay content. Silty carbonate indicates the highest carbonate content with the least clay content. In an order of mudstone, calcareous siltstone, and silty carbonate, rebound hardness and Young’s modulus show an increasing trend. As a result of rock-fluid interactions, there are potential scaling reactions during completion and production that could ultimately affect permeability and production rates. Overall, the proposed multiscale integration approach is critical for the geologic characterization of most rocks. However, in shale reservoirs dominated by microporosity and microstructure where engineered fractures are expected to provide permeability at a reservoir scale, successful integration is essential. An optimized, integrated geological characterization of the Caney Shale that is well aligned with the engineering designs in drilling, completing, and producing wellbores will ultimately lead to optimal production while providing safe and environmentally responsible operations.

Wang, Yulun↗
DOE OSTI2025

Coupling magnons to an opto-electronic parametric oscillator

Hybrid magnonic systems have emerged as versatile modular components for quantum signal transduction and sensing applications owing to their capability of connecting distinct quantum platforms. To date, the majority of the magnonic systems have been explored in a local, near-field scheme, due to the close proximity required for realizing a strong coupling between magnons and other excitations. This constraint greatly limits the applicability of magnons in developing remotely coupled, distributed quantum network systems. On the contrary, opto-electronic architectures hosting self-sustained oscillations have been a unique platform for long-haul signal transmission and processing. Here, we integrated an opto-electronic oscillator with a magnonic oscillator consisting of a microwave waveguide and a Y 3 Fe 5 O 12 (YIG) sphere, and demonstrated strong and coherent coupling between YIG’s magnon modes and the opto-electronic oscillator’s characteristic photon modes—revealing the hallmark anti-crossing gap in the measured spectrum. In particular, the photon mode is produced on-demand via a nonlinear, parametric process as stipulated by an external seed pump. Both the internal cavity phase and the external pump phase can be precisely tuned to stabilize either degenerate or nondegenerate auto-oscillations. Our result lays out a new, hybrid platform for investigating the long-distance coupling and nonlinearity in coherent magnonic phenomena, which may be found useful in constructing the future “distributed hybrid magnonic systems.”

36 MATERIALS SCIENCE↗
DOE OSTI2022

Unified Language Frontend for Physic-Informed AI/ML

Artificial intelligence and machine learning (AI/ML) are becoming important tools for scientific modeling and simulation as in several other fields such as image analysis and natural language processing. ML techniques can leverage the computing power available in modern systems and reduce the human effort needed to configure experiments, interpret and visualize results, draw conclusions from huge quantities of raw data, and build surrogates for physics based models. Domain scientists in fields like fluid dynamics, microelectronics and chemistry can automate many of their most difficult and repetitive tasks or improve the design times by use of the faster ML-surrogates. However, modern ML and traditional scientific highperformance computing (HPC) tend to use completely different software ecosystems. While ML frameworks like PyTorch and TensorFlow provide Python APIs, most HPC applications and libraries are written in C++. Direct interoperability between the two languages is possible but is tedious and error-prone. In this work, we show that a compiler-based approach can bridge the gap between ML frameworks and scientific software with less developer effort and better efficiency. We use the MLIR (multi-level intermediate representation) ecosystem to compile a pre-trained convolutional neural network (CNN) in PyTorch to freestanding C++ source code in the Kokkos programming model. Kokkos is a programming model widely used in HPC to write portable, shared-memory parallel code that can natively target a variety of CPU and GPU architectures. Our compiler-generated source code can be directly integrated into any Kokkosbased application with no dependencies on Python or cross-language interfaces.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗