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DOE OSTI2025

CRCNS22 Learning Rules in the Hippocampus and their Mapping to Neuromorphic Systems (Final Technical Report)

Large scale biologically-realistic computational models are key to investigating the interplay between structure and function in nervous systems, thus paving the way to new clinical methods and neuro-inspired computing solutions. This project focuses on the hippocampus, in particular the CA3-CA1 regions, due to their role in associative learning and memory, pattern separation and completion, and spatial navigation. Investigations into the neuronal organization and learning rule(s) of this circuit can shed light into how declarative memories are formed, stored, recalled and forgotten and inform computational, experimental and clinical neuroscience work. Our project aims at developing a novel data-driven methodology supported by a broad heterogeneous base of neuroscience experimental knowledge and inspired from advances in computer science and engineering. Specifically, this work will benchmark existing and new learning rules within a full-scale spiking neural network simulation of the CA3-CA1 region. The model will be based on an open-source repository, called the Hippocampome, which contains neuronal morphologies, firing patterns, synapse probabilities, and most other required parameters for all known neuron types in the rodent hippocampal formation. The model will be first trained in a supervised fashion for associative memory tasks using backpropagation through time traditionally used in computer science, enhanced with a new technique called the surrogate gradient method. This optimization method will be used to obtain a global loss minimization, but it is not biologically inspired as it assumes the use of data not locally available to the synapses. However, we propose its use as a benchmarking tool, to compare the training performance of local biologically plausible and hardware-mappable learning rules at scale. New rules or combinations will be proposed and tested as needed, based on the obtained results. Progress in this area will also drive the development of novel hardware-mappable algorithms for continual lifelong learning and categorization of new events from few presented examples. This project goes beyond the existing state-of-the-art by looking at large scale realistic neuronal circuits as networks trainable via global optimization methods such as surrogate gradient descent. The objective function of the brain that supports learning is largely unknown, but it is likely that it operates through local learning rules. Studying network trajectories around local minima as proposed in this work represents a useful strategy for understanding whether a network is training by using a specific (set of) learning rule(s). Starting from a completely untrained network is a challenging test since it is difficult to determine how the learning rule affects the trajectory of the network. This interdisciplinary project will help understand what rule governs learning in these regions or if multiple learning rules are involved. The work will develop a robust methodology to measure if the network is converging to the target solution, oscillating around it, or diverging away.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Validation of a Custom Ball-on-Ring Apparatus and Consideration of Common Issues for Use in Further Testing (SULI Deliverables)

Ceramic materials are well-known for their high hardness and strength but are limited in their application due to low toughness and sudden failure. As a potential solution, inspiration can be taken from dental enamel nanostructure, where undulating rods cause cracks to branch or deflect, increasing the energy needed to cause total fracture of a ceramic part. Following the dental enamel structure, a novel ceramic which uses 3D printed Yttria stabilized Zirconia rods in an alumina matrix was developed. To test this bio-inspired ceramic material, a proper testing apparatus needed to be created and tested to verify its accuracy. For this project, a bespoke ball on ring testing apparatus was created and tested using both conventionally sintered alumina disks and purchased alumina disks to validate its accuracy. By comparing the Weibull distribution of rupture strengths measured by the tests to literature values, it was shown that the testing frame had a wide distribution of strength which did not align with literature values on the lower end. Through fractography, it was found that some samples fractured from the contact stress induced by the ball indenter, which could not be used to calculate rupture strength. This fracture was often linked to low stress to failure, which was initiated by a flaw on the surface near the indenter which acted as a stress concentrator. Removal of these samples from the data set increased the accuracy of the reported rupture strength values for the ceramic. Considering the equations for the magnitude of contact and flexural strength, along with observations of initiating flaws, several measures can be taken for testing the bio-inspired composite. These measures include proper polishing of both sides of the sample, reducing sample thickness, and potentially using a softer indenter material.

36 - MATERIALS SCIENCE↗

DOE OSTI2026

CRADA Number NFE-24-10110 with Qubit Engineering Inc. (CRADA Final Report)

Over the past year, the Qubit Engineering team has pushed the frontiers of power‑grid optimization, working in close collaboration with Oak Ridge National Laboratory (ORNL) and the Tennessee Valley Authority (TVA). Their progress is reflected in three newly submitted conference papers, “Unified Relational GNN Architecture for AC Optimal Power Flow Calculations in Electric Grids,” “Graph‑Based Attention Mechanisms for Solving the AC Optimal Power Flow Problem in Electrical‑Power Networks,” and “Enhanced Power‑Grid Maintenance Planning and Quantum‑Inspired Combinatorial Prospects.” These publications showcase state‑of‑the‑art graph‑neural‑network methods for AC‑OPF and novel quantum‑inspired heuristics for maintenance scheduling. Beyond the academic results, the Qubit team has converted the research into two production‑grade tools built on TVA data: Neuro‑Grid, an AI‑driven power‑flow simulator that provides instant, interactive full‑grid load‑flow visualizations, and Quanta‑Grid, a quantum‑inspired maintenance‑scheduling engine to support logistics optimization for power utilities. Together, these advances demonstrate how Qubit’s partnership with ORNL and TVA is delivering practical, physics‑grounded analytics for next‑generation grid management.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

DEEP CELLULAR RECURRENT NEURAL ARCHITECTURE FOR EFFICIENT MULTIDIMENSIONAL TIME-SERIES DATA PROCESSING

Efficient processing of time series data is a fundamental yet challenging problem in pattern recognition. Though recent developments in machine learning and deep learning have enabled remarkable improvements in processing large scale datasets in many application domains, most are designed and regulated to handle inputs that are static in time. Many real-world data, such as in biomedical, surveillance and security, financial, manufacturing and engineering applications, are rarely static in time, and demand models able to recognize patterns in both space and time. Current machine learning (ML) and deep learning (DL) models adapted for time series processing tend to grow in complexity and size to accommodate the additional dimensionality of time. Specifically, the biologically inspired learning based models known as artificial neural networks that have shown extraordinary success in pattern recognition, tend to grow prohibitively large and cumbersome in the presence of large scale multi-dimensional time series biomedical data such as EEG. Consequently, this work aims to develop representative ML and DL models for robust and efficient large scale time series processing. First, we design a novel ML pipeline with efficient feature engineering to process a large scale multi-channel scalp EEG dataset for automated detection of epileptic seizures. With the use of a sophisticated yet computationally efficient time-frequency analysis technique known as harmonic wavelet packet transform and an efficient self-similarity computation based on fractal dimension, we achieve state-of-the-art performance for automated seizure detection in EEG data. Subsequently, we investigate the development of a novel efficient deep recurrent learning model for large scale time series processing. For this, we first study the functionality and training of a biologically inspired neural network architecture known as cellular simultaneous recurrent neural network (CSRN). We obtain a generalization of this network for multiple topological image processing tasks and investigate the learning efficacy of the complex cellular architecture using several state-of-the?art training methods. Finally, we develop a novel deep cellular recurrent neural network (CDRNN) architecture based on the biologically inspired distributed processing used in CSRN for processing time series data. The proposed DCRNN leverages the cellular recurrent architecture to promote extensive weight sharing and efficient, individualized, synchronous processing of multi-source time series data. Experiments on a large scale multi-channel scalp EEG, and a machine fault detection dataset show that the proposed DCRNN offers state-of-the-art recognition performance while using substantially fewer trainable recurrent units.

Vidyaratne, Lasitha S.↗

DOE OSTI2021

Project Earthrise: Proceedings of the Ninth Annual Conference of in VIVO Planetary Health

The “Earthrise” photograph, taken on the 1968 Apollo 8 mission, became one of the most significant images of the 20th Century. It triggered a profound shift in environmental awareness and the potential for human unity—inspiring the first Earth Day in 1970. Taking inspiration from these events 50 years later, we initiated Project Earthrise at our 2020 annual conference of inVIVO Planetary Health. This builds on the emergent concept of planetary health, which provides a shared narrative to integrate rich and diverse approaches from all aspects of society towards shared solutions to global challenges. The acute catastrophe of the COVID-19 pandemic has drawn greater attention to many other interconnected global health, environmental, social, spiritual, and economic problems that have been underappreciated or neglected for decades. This is accelerating opportunities for greater collaborative action, as many groups now focus on the necessity of a “Great Transition”. While ambitious integrative efforts have never been more important, it is imperative to apply these with mutualistic value systems as a compass, as we seek to make wiser choices. Project Earthrise is our contribution to this important process. This underscores the imperative for creative ecological solutions to challenges in all systems, on all scales with advancing global urbanization in the digital age—for personal, environmental, economic and societal health alike. At the same time, our agenda seeks to equally consider our social and spiritual ecology as it does natural ecology. Revisiting the inspiration of “Earthrise”, we welcome diverse perspectives from across all dimensions of the arts and the sciences, to explore novel solutions and new normative values. Building on academic rigor, we seek to place greater value on imagination, kindness and mutualism as we address our greatest challenges, for the health of people, places and planet.

interdependence↗

DOE OSTI2025

The Multiple Paths to Merger of Unequal-mass Black Hole Binaries in the Disks of Active Galactic Nuclei

The accretion disks that power active galactic nuclei (AGN) are thought to house populations of stars and compact objects; after forming binaries these compact objects may merge, begetting gravitational waves (GWs) such as those detected by LIGO and VIRGO. We present a comprehensive study of the early evolution of binaries within AGN disks as their orbits are influenced by the surrounding gas, focusing on eccentric and unequal-mass binaries. Nearly equal-mass binaries behave similarly to their equal-mass counterparts: Prograde binaries inspiral, albeit somewhat slowly, and have their eccentricities damped; retrograde binaries inspiral ∼2–3 times faster than their prograde counterparts, and those with near-equal masses are driven quickly toward near-unity eccentricities. However, the primaries in retrograde binaries with mass ratios of m 2 /m 1 ≲ 0.4 experience significantly weaker headwinds and retain substantial accretion disks that help damp binary eccentricities, slowing binary inspirals. Additionally, we find that while accretion drives prograde binaries toward equal masses thanks to the exchange of material between the primary and secondary accretion disks, retrograde binaries are driven slowly toward more extreme mass ratios. Prograde binaries, and generally those with low mass ratios, likely accrete for multiple e-folding timescales before merger. On the other hand, high-mass-ratio retrograde binaries may merge before accreting substantially, potentially approaching merger with detectable eccentricity. Future ground-based GW observatories, with their broader frequency coverage, should be particularly useful for studying these populations.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2023

Halide Perovskites and Their Derivatives for Efficient, High‐Resolution Direct Radiation Detection: Design Strategies and Applications

Abstract The past decade has witnessed a rapid rise in the performance of optoelectronic devices based on lead‐halide perovskites (LHPs). The large mobility‐lifetime products and defect tolerance of these materials, essential for optoelectronics, also make them well‐suited for radiation detectors, especially given the heavy elements present, which is essential for strong X‐ray and γ‐ray attenuation. Over the past decade, LHP thick films, wafers, and single crystals have given rise to direct radiation detectors that have outperformed incumbent technologies in terms of sensitivity (reported values up to 3.5 × 10 6 µC Gy air −1 cm −2 ), limit of detection (directly measured values down to 1.5 nGy air s −1 ), along with competitive energy and imaging resolution at room temperature. At the same time, lead‐free perovskite‐inspired materials (e.g., methylammonium bismuth iodide), which have underperformed in solar cells, have recently matched and, in some areas (e.g., in polarization stability), surpassed the performance of LHP detectors. These advances open up opportunities to achieve devices for safer medical imaging, as well as more effective non‐invasive analysis for security, nuclear safety, or product inspection applications. Herein, the principles behind the rapid rises in performance of LHP and perovskite‐inspired material detectors, and how their properties and performance link with critical applications in non‐invasive diagnostics are discussed. The key strategies to engineer the performance of these materials, and the important challenges to overcome to commercialize these new technologies are also discussed.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Dendritic Computing with Multigate Ferroelectric Field-Effect Transistors

Although inspired by neuronal systems in the brain, artificial neural networks generally employ point-neurons, which offer computational complexity far less than that of their biological counterparts. Neurons have dendritic arbors that connect to different sets of synapses and offer local nonlinear accumulation – playing a pivotal role in processing and learning. Inspired by this, we propose a novel neuron design based on a multigate ferroelectric field-effect transistor that mimics dendrites. It leverages ferroelectric nonlinearity for local computations within dendritic branches while utilizing the transistor action to generate the neuronal output. The branched architecture enables smaller crossbar arrays in hardware integration, improving efficiency. Using an experimentally calibrated device-circuit-algorithm cosimulation framework, we demonstrate that networks incorporating our dendritic neurons achieve superior performance compared to much larger networks without dendrites (∼ 17× fewer trainable weight parameters). These findings suggest that dendritic hardware can significantly improve computational efficiency and learning capacity of neuromorphic systems optimized for edge applications.

36 MATERIALS SCIENCE↗

DOE OSTI2022

ASTRO: Facilitating Advancements in Low-Impact Solar Research, Deployment, and Dissemination

The U.S. Department of Energy's (DOE) InSPIRE project evaluates opportunities for cost reductions and assesses the environmental compatibility of solar energy technologies through low environmental impact designs and approaches. To achieve the project's aim, DOE brings together researchers from the National Renewable Energy Laboratory, Argonne National Laboratory, universities, local governments, environmental groups, and solar industry partners to conduct field-based research complemented by foundational analytical studies. The Agriculture and Solar Together: Research Opportunities (ASTRO) advisory group members come from across the United States and represent leading solar industry partners, state agencies, vegetation management companies, and other organizations focused on research, food and agriculture, and the environment. The ASTRO group of engaged advisors is a complementary combination of organizations creating positive feedback loops that spark and solidify new connections, accelerate information dissemination, and magnify the impact of the InSPIRE project and associated low-impact solar research initiatives.

14 SOLAR ENERGY↗

DOE OSTI2020

A Computational Theory for the Emergence of Grammatical Categories in Cortical Dynamics

A general agreement in psycholinguistics claims that syntax and meaning are unified precisely and very quickly during online sentence processing. Although several theories have advanced arguments regarding the neurocomputational bases of this phenomenon, we argue that these theories could potentially benefit by including neurophysiological data concerning cortical dynamics constraints in brain tissue. In addition, some theories promote the integration of complex optimization methods in neural tissue. In this paper we attempt to fill these gaps introducing a computational model inspired in the dynamics of cortical tissue. In our modeling approach, proximal afferent dendrites produce stochastic cellular activations, while distal dendritic branches–on the other hand–contribute independently to somatic depolarization by means of dendritic spikes, and finally, prediction failures produce massive firing events preventing formation of sparse distributed representations. The model presented in this paper combines semantic and coarse-grained syntactic constraints for each word in a sentence context until grammatically related word function discrimination emerges spontaneously by the sole correlation of lexical information from different sources without applying complex optimization methods. By means of support vector machine techniques, we show that the sparse activation features returned by our approach are well suited—bootstrapping from the features returned by Word Embedding mechanisms—to accomplish grammatical function classification of individual words in a sentence. In this way we develop a biologically guided computational explanation for linguistically relevant unification processes in cortex which connects psycholinguistics to neurobiological accounts of language. We also claim that the computational hypotheses established in this research could foster future work on biologically-inspired learning algorithms for natural language processing applications.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Patterned Quasi-Liquid Surfaces for Condensation of Low Surface Tension Fluids

Extensive research concerns dropwise condensation of low surface tension fluids to promote energy efficiency and decarbonization in thermal energy systems. However, it is challenging as these fluids typically result in filmwise condensation. Drawing inspiration from the Namib desert beetle that enhances condensation through patterned wettability, conventional beetle-inspired surfaces excel in water condensation but flood when condensing low surface tension fluids. In this work, a patterned quasi-liquid surface is reported that achieves exceptional dropwise condensation of low surface tension fluids. The surface consists of alternating stripes with low surface energy, that is, a perfluoropolyether (PFPE) and fluorinated quasi-liquid surface (FQLS), that shows ultralow contact angle hysteresis for ethanol and hexane. Further, the PFPE stripes are slightly more slippery, acting as slippery bridges that accelerate droplet coalescence and removal. It is experimentally demonstrated that the striped PFPE-FQLS pattern exhibits a heat transfer coefficient 85%, 330%, and 550% higher than that of PFPE, fluorinated silane, and filmwise condensation, respectively. This study reveals that a high contact angle is desired to sustain dropwise condensation, irrespective of contact angle hysteresis. These findings provide a new paradigm for promoting the dropwise condensation of low surface tension fluids and offer valuable insights into surface design for energy sustainability.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Probabilistic Neural Computing with Stochastic Devices

Abstract The brain has effectively proven a powerful inspiration for the development of computing architectures in which processing is tightly integrated with memory, communication is event‐driven, and analog computation can be performed at scale. These neuromorphic systems increasingly show an ability to improve the efficiency and speed of scientific computing and artificial intelligence applications. Herein, it is proposed that the brain's ubiquitous stochasticity represents an additional source of inspiration for expanding the reach of neuromorphic computing to probabilistic applications. To date, many efforts exploring probabilistic computing have focused primarily on one scale of the microelectronics stack, such as implementing probabilistic algorithms on deterministic hardware or developing probabilistic devices and circuits with the expectation that they will be leveraged by eventual probabilistic architectures. A co‐design vision is described by which large numbers of devices, such as magnetic tunnel junctions and tunnel diodes, can be operated in a stochastic regime and incorporated into a scalable neuromorphic architecture that can impact a number of probabilistic computing applications, such as Monte Carlo simulations and Bayesian neural networks. Finally, a framework is presented to categorize increasingly advanced hardware‐based probabilistic computing technologies.

Misra, Shashank↗

DOE OSTI2025

Artificial Intelligence and Multiscale Modeling for Sustainable Biopolymers and Bioinspired Materials

Abstract Biopolymers and bioinspired materials contribute to the construction of intricate hierarchical structures that exhibit advanced properties. The remarkable toughness and damage tolerance of such multilevel materials are conferred through the hierarchical assembly of their multiscale (i.e., atomistic to macroscale) components and architectures. Here, the functionality and mechanisms of biopolymers and bio‐inspired materials at multilength scales are explored and summarized, focusing on biopolymer nanofibril configurations, biocompatible synthetic biopolymers, and bio‐inspired composites. Their modeling methods with theoretical basis at multiple lengths and time scales are reviewed for biopolymer applications. Additionally, the exploration of artificial intelligence‐powered methodologies is emphasized to realize improvements in these biopolymers from functionality, biodegradability, and sustainability to their characterization, fabrication process, and superior designs. Ultimately, a promising future for these versatile materials in the manufacturing of advanced materials across wider applications and greater lifecycle impacts is foreseen.

Wang, Xing Quan [Department of Mechanical Engineer↗

DOE OSTI2023

Nanostructure‐Derived Antireflectivity in Leafhopper Brochosomes

Understanding how insect‐derived biomaterials interact with light has led to new advances and interdisciplinary insights in entomology and physics. Leafhoppers are insects that coat themselves with highly ordered biological nanostructures known as brochosomes. Brochosomes are thought to provide a range of protective properties to leafhoppers, such as hydrophobicity and antireflectivity, which has inspired the development of synthetic brochosomes that mimic their structures. Despite recent progress, the high antireflective properties of brochosome structures are not fully understood. Herein, a combination of experiments and computational modeling is used to understand the structure‐, material‐, and polarization‐dependent optical properties of brochosomes modeled on the geometries found in three leafhopper species. The results qualitatively represent that light interference interaction with nanostructures naturally occurring in brochosomes is responsible for the spectral tuning and the asymmetric line shape of the reflectance spectra. Whereas prior work has focused on the computational modeling of idealized pitted particles, this work shows that light–matter interactions with brochosome structures can be tuned by varying the geometry of their cage‐like nanoscale features and by changing the arrangement of multiparticle assemblies. Broadly, this work establishes principles for the guided design of new optically active materials inspired by these unique insect nanostructures.

Banerjee, Progna↗

DOE OSTI2021

A Review on Bioinspired Proton Exchange Membrane Fuel Cell: Design and Materials

With the climate crisis gathering recognition, there has been significant interest in clean energy. As a reliable and clean energy solution, markets transitioning toward sustainable energy have identified polymer electrolyte membrane fuel cells (PEMFCs) as a critical technology. The increasing interest in PEMFCs is primarily due to their high energy density, high efficiency and zero greenhouse gas emission. However, additional development is required to overcome the current barriers associated with fuel cell component design, high manufacturing cost, and insufficient stability. Bioinspired designs have the potential to provide new and valuable insights using novel techniques for extracting design inspirations from biological structures. Applying these design concepts to develop materials and structures for various fuel cell components can be transformative. The well–adapted structures and functional features of biological systems have been proven to greatly enhance the performance of current designs through novel and optimized pathways. Bioinspired designs have indeed lived up to performance expectations and even exceed them. Herein, the potential and value of using these bioinspired designs are discussed. Recent studies and advancements based on nature–inspired structures for components of PEMFCs and how these design considerations substantiate their claim as an attractive alternative to conventional designs are discussed.

25 ENERGY STORAGE↗

DOE OSTI2021

Bioinspired Activation of N 2 on Asymmetrical Coordinated Fe Grafted 1T MoS 2 at Room Temperature

Inspired by the nitrogen fixation process on MoFe nitrogenase, asymmetrical coordinated Fe grafted onto 1T MoS 2 were successfully synthesized. The unique electron -rich structure with asymmetrical coordination made the 1T Fe 0.1 Mo 0.9 S 2 layered material actively react with water and dinitrogen at room temperature and atmosphere pressure. Subsequently, ammonia can be produced with a yield of 800 pmol (NH 4 + ) g -1 (12.5% yield in mole). The activation, fixation and reduction of dinitrogen were confirmed by isotopically labeled experiments. The location and the specific coordination environment of grafted Fe in Fe-Mo-S were further determined by X-ray absorption spectroscopy analysis. Our work demonstrates that the nitrogen fixation and reduction for ammonia at room temperature without any chemical and electrochemical assistance is distinctly different from traditional bionic -inspired nitrogen fixation process. The mechanism of the activation and reduction of N 2 was further investigated by density functional theory calculation and Raman spectra. Compared with 1T MoS 2 , the enriched electron nature and asymmetrical coordination of Fe in Fe-Mo-S materials play a critical role in the bioinspired activation of N 2 at ambient condition.

1T MoS2↗

DOE OSTI2024

Corners and islands in the S-matrix bootstrap of the open superstring

We bootstrap the Veneziano superstring amplitude in 10 dimensions from the bottom-up. Starting with the most general maximally supersymmetric Yang-Mills EFT, we input information about the lowest-lying massive states, which we assume contribute via tree-level exchanges to the 4-point amplitude. We show the following: (1) if there is only a single state at the lowest mass, it must be a scalar. (2) Assuming a string-inspired gap between the mass of this scalar and any other massive states, the allowed region of Wilson coefficients has a new sharp corner where the Veneziano amplitude is located. (3) Upon fixing the next massive state to be a vector, the EFT bounds have a one-parameter family of corners; these would correspond to models with linear Regge trajectories of varying slopes, one of which is the open superstring. (4) When the ratio between the massive scalar coupling and the tr F 4 coefficient is fixed to its string value, the spin and mass of the second massive state is determined by the bootstrap and the Veneziano amplitude is isolated on a small island in parameter space. Finally, we compare with other recent bootstraps approaches, both the pion model and imposing Regge-inspired maximal spin constraints.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2021

Q-balls meet fuzzballs: non-BPS microstate geometries

We construct a three-parameter family of non-extremal microstate geometries, or “microstrata”, that are dual to states and deformations of the D1-D5 CFT. These families are non-extremal analogues of superstrata. We find these microstrata by using a Q-ball-inspired Ansatz that reduces the equations of motion to solving for eleven functions of one variable. We then solve this system both perturbatively and numerically and the results match extremely well. We find that the solutions have normal mode frequencies that depend upon the amplitudes of the excitations. We also show that, at higher order in perturbations, some of the solutions, having started with normalizable modes, develop a “non-normalizable” part, suggesting that the microstrata represent states in a perturbed form of the D1-D5 CFT. This paper is intended as a “Proof of Concept” for the Q-ball-inspired approach, and we will describe how it opens the way to many interesting follow-up calculations both in supergravity and in the dual holographic field theory.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗