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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Superconducting neural networks with disordered Josephson junction array synaptic networks and leaky integrate-and-fire loop neurons

Fully coupled randomly disordered recurrent superconducting networks with additional open-ended channels for inputs and outputs are considered the basis to introduce a new architecture to neuromorphic computing in this work. Various building blocks of such a network are designed around disordered array synaptic networks using superconducting devices and circuits as an example, while emphasizing that a similar architectural approach may be compatible with several other materials and devices. A multiply coupled (interconnected) disordered array of superconducting loops containing Josephson junctions [equivalent to superconducting quantum interference devices (SQUIDs)] forms the aforementioned collective synaptic network that forms a fully recurrent network together with compatible neuron-like elements and feedback loops, enabling unsupervised learning. This approach aims to take advantage of superior power efficiency, propagation speed, and synchronizability of a small world or a random network over an ordered/regular network. Additionally, it offers a significant factor of increase in scalability. Here, a compatible leaky integrate-and-fire neuron made of superconducting loops with Josephson junctions is presented, along with circuit components for feedback loops as needed to complete the recurrent network. Several of these individual disordered array neural networks can further be coupled together in a similarly disordered way to form a hierarchical architecture of recurrent neural networks that is often suggested as similar to a biological brain.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

A novel approach to partitioning evapotranspiration into evaporation and transpiration in flooded ecosystems

Reliable partitioning of micrometeorologically measured evapotranspiration (ET) into evaporation (E) and transpiration (T) would greatly enhance our understanding of the water cycle and its response to climate change related shifts in local-to-regional climate conditions and rising global levels of vapor pressure deficit (VPD). While some methods on ET partitioning have been developed, their underlying assumptions make them difficult to apply more generally, especially in sites with large contributions of E. Here, we report a novel ET partitioning method using artificial neural networks (ANNs) in combination with a range of environmental input variables to predict daytime E from nighttime ET measurements. The study uses eddy covariance data from four restored wetlands in the Sacramento-San Joaquin Delta, California, USA, as well as leaf-level T data for validation. The four wetlands vary in their vegetation make-up and structure, representing a range of ET conditions. The ANNs were built with increasing complexity by adding the input variable that resulted in the next highest average value of model testing R 2 across all sites. The order of variable inclusion (and importance) was: VPD > gap-filled sensible heat flux (H_gf) > air temperature (T air ) > friction velocity (u * ) > other variables. The model using VPD, H_gf, T air , and u * showed the best performance during validation with independent data and had a mean testing R 2 value of 0.853 (averaged across all sites, range from 0.728 to 0.910). In comparison to other methods, our ANN method generated T/ET partitioning results which were more consistent with CO 2 exchange data especially for more heterogeneous sites with large E contributions. Further, our method improves the understanding of T/ET partitioning. While it may be particularly suited to flooded ecosystems, it can also improve T/ET partitioning in other systems, increasing our knowledge of the global water cycle and ecosystem functioning.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Accurate, Uncertainty-Aware Classification of Molecular Chemical Motifs from Multimodal X-ray Absorption Spectroscopy

Accurate classification of molecular chemical motifs from experimental measurement is an important problem in molecular physics, chemistry, and biology. In this work, we present neural network ensemble classifiers for predicting the presence (or lack thereof) of 41 different chemical motifs on small molecules from simulated C, N, and O K-edge X-ray absorption near-edge structure (XANES) spectra. Our classifiers not only achieve class-balanced accuracies of more than 0.95 but also accurately quantify uncertainty. Here, we also show that including multiple XANES modalities improves predictions notably on average, demonstrating a “multimodal advantage” over any single modality. In addition to structure refinement, our approach can be generalized to broad applications with molecular design pipelines.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Photothermally Activated Artificial Neuromorphic Synapses

Biological nervous systems rely on the coordination of billions of neurons with complex, dynamic connectivity to enable the ability to process information and form memories. In turn, artificial intelligence and neuromorphic computing platforms have sought to mimic biological cognition through software-based neural networks and hardware demonstrations utilizing memristive circuitry with fixed dynamics. Here, to incorporate the advantages of tunable dynamic software implementations of neural networks into hardware, we develop a proof-of-concept artificial synapse with adaptable resistivity. This synapse leverages the photothermally induced local phase transition of VO 2 thin films by temporally modulated laser pulses. Such a process quickly modifies the conductivity of the film site-selectively by a factor of 500 to “activate” these neurons and store “memory” by applying varying bias voltages to induce self-sustained Joule heating between electrodes after activation with a laser. These synapses are demonstrated to undergo a complete heating and cooling cycle in less than 120 ns.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Prediction of Specificity of α-Conotoxins to Subtypes of Human Nicotinic Acetylcholine Receptors with Semi-supervised Machine Learning

Conotoxins are a family of highly toxic neurotoxins composed of cysteine-rich peptides produced by marine cone snails. The most lethal cone snail species to humans is Conus geographus, with fatality rates of up to ∼65% from a single sting, which is caused mostly by the activity of α-conotoxins against human nicotinic acetylcholine receptors (nAChRs). While sequence-based machine learning (ML) classifiers have been trained to identify targets of conotoxins binding voltage-gated ion channels, no ML model has been built to predict the subtype-specific nAChR targets of α-conotoxins. Here, we trained an ML model in a semi-supervised manner to predict the specificity of α-conotoxin binding toward different human nAChR subtypes to overcome the challenge of limited data in subtype-specific nAChR targets of α-conotoxins and the issue that one α-conotoxin can bind multiple nAChR subtypes with high selectivity. We considered additional features of sequences of α-conotoxins in training our ML model, including the secondary structure propensities and electrostatic properties, which resulted in better prediction capability for the ML model. Notably, we identify that most α-conotoxins bind to α3β2, α1γδ, and α7 subtypes of human nAChRs. Our findings from this study provide a framework for predicting targets of various kinds of toxins.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Design of a Robust Memristive Spiking Neuromorphic System with Unsupervised Learning in Hardware

Spiking neural networks (SNN) offer a power efficient, biologically plausible learning paradigm by encoding information into spikes. The discovery of the memristor has accelerated the progress of spiking neuromorphic systems, as the intrinsic plasticity of the device makes it an ideal candidate to mimic a biological synapse. Despite providing a nanoscale form factor, non-volatility, and low-power operation, memristors suffer from device-level non-idealities, which impact system-level performance. To address these issues, this article presents a memristive crossbar-based neuromorphic system using unsupervised learning with twin-memristor synapses, fully digital pulse width modulated spike-timing-dependent plasticity, and homeostasis neurons. Additionally, the implemented single-layer SNN was applied to a pattern-recognition task of classifying handwritten-digits. The performance of the system was analyzed by varying design parameters such as number of training epochs, neurons, and capacitors. Furthermore, the impact of memristor device non-idealities, such as device-switching mismatch, aging, failure, and process variations, were investigated and the resilience of the proposed system was demonstrated.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Rethinking skip connections in Spiking Neural Networks with Time-To-First-Spike coding

Time-To-First-Spike (TTFS) coding in Spiking Neural Networks (SNNs) offers significant advantages in terms of energy efficiency, closely mimicking the behavior of biological neurons. In this work, we delve into the role of skip connections, a widely used concept in Artificial Neural Networks (ANNs), within the domain of SNNs with TTFS coding. Our focus is on two distinct types of skip connection architectures: (1) addition-based skip connections, and (2) concatenation-based skip connections. We find that addition-based skip connections introduce an additional delay in terms of spike timing. On the other hand, concatenation-based skip connections circumvent this delay but produce time gaps between after-convolution and skip connection paths, thereby restricting the effective mixing of information from these two paths. To mitigate these issues, we propose a novel approach involving a learnable delay for skip connections in the concatenation-based skip connection architecture. This approach successfully bridges the time gap between the convolutional and skip branches, facilitating improved information mixing. We conduct experiments on public datasets including MNIST and Fashion-MNIST, illustrating the advantage of the skip connection in TTFS coding architectures. Additionally, we demonstrate the applicability of TTFS coding on beyond image recognition tasks and extend it to scientific machine-learning tasks, broadening the potential uses of SNNs.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2004

Approach for Autonomous Control of Unmanned Aerial Vehicle Using Intelligent Agents for Knowledge Creation

This paper describes the development of a planned approach for Autonomous operation of an Unmanned Aerial Vehicle (UAV). A Hybrid approach will seek to provide Knowledge Generation through the application of Artificial Intelligence (AI) and Intelligent Agents (IA) for UAV control. The applications of several different types of AI techniques for flight are explored during this research effort. The research concentration is directed to the application of different AI methods within the UAV arena. By evaluating AI and biological system approaches. which include Expert Systems, Neural Networks. Intelligent Agents, Fuzzy Logic, and Complex Adaptive Systems, a new insight may be gained into the benefits of AI and CAS techniques applied to achieving true autonomous operation of these systems. Although flight systems were explored, the benefits should apply to many Unmanned Vehicles such as: Rovers. Ocean Explorers, Robots, and autonomous operation systems. A portion of the flight system is broken down into control agents that represent the intelligent agent approach used in AI. After the completion of a successful approach, a framework for applying an intelligent agent is presented. The initial results from simulation of a security agent for communication are presented.

Dufrene, Warren R., Jr.↗
DOE OSTI2020

A self-supervised workflow for particle picking in cryo-EM

High-resolution single-particle cryo-EM data analysis relies on accurate particle picking. To facilitate the particle picking process, a self-supervised workflow has been developed. This includes an iterative strategy, which uses a 2D class average to improve training particles, and a progressively improved convolutional neural network for particle picking. To automate the selection of particles, a threshold is defined (%/Res) using the ratio of percentage class distribution and resolution as a cutoff. This workflow has been tested using six publicly available data sets with different particle sizes and shapes, and can automatically pick particles with minimal user input. The picked particles support high-resolution reconstructions at 3.0 Å or better. This workflow is a step towards automated single-particle cryo-EM data analysis at the stage of particle picking. It may be used in conjunction with commonly used single-particle analysis packages such as Relion , cryoSPARC , cisTEM , SPHIRE and EMAN2 .

2D class averages↗
DOE OSTI2025

NeuroCoreX: Brain-Inspired Computing from Code to Circuit

NeuroCoreX is an open-source codebase that enables the implementation of brain-inspired, energy-efficient neuromorphic computing models on FPGA hardware. Designed to support real-time learning, all-to-all neural connectivity, and flexible network architectures, NeuroCoreX offers a hands-on, accessible platform for exploring biologically inspired models of neural computation. It empowers researchers, students, and developers to implement and experiment with adaptive systems—bringing the power of neuromorphic computing to a broader community through a low-cost, scalable, and reconfigurable framework.

Gautam, Ashish [Oak Ridge National Laboratory (ORN↗
NASA NTRS1988

A survey of visual preprocessing and shape representation techniques

Many recent theories and methods proposed for visual preprocessing and shape representation are summarized. The survey brings together research from the fields of biology, psychology, computer science, electrical engineering, and most recently, neural networks. It was motivated by the need to preprocess images for a sparse distributed memory (SDM), but the techniques presented may also prove useful for applying other associative memories to visual pattern recognition. The material of this survey is divided into three sections: an overview of biological visual processing; methods of preprocessing (extracting parts of shape, texture, motion, and depth); and shape representation and recognition (form invariance, primitives and structural descriptions, and theories of attention).

Olshausen, Bruno A.↗
DOE OSTI2024

Utilization of Synthetic Near-Infrared Spectra via Generative Adversarial Network to Improve Wood Stiffness Prediction

Near-infrared (NIR) spectroscopy is widely used as a nondestructive evaluation (NDE) tool for predicting wood properties. When deploying NIR models, one faces challenges in ensuring representative training data, which large datasets can mitigate but often at a significant cost. Machine learning and deep learning NIR models are at an even greater disadvantage because they typically require higher sample sizes for training. In this study, NIR spectra were collected to predict the modulus of elasticity (MOE) of southern pine lumber (training set = 573 samples, testing set = 145 samples). To account for the limited size of the training data, this study employed a generative adversarial network (GAN) to generate synthetic NIR spectra. The training dataset was fed into a GAN to generate 313, 573, and 1000 synthetic spectra. The original and enhanced datasets were used to train artificial neural networks (ANNs), convolutional neural networks (CNNs), and light gradient boosting machines (LGBMs) for MOE prediction. Overall, results showed that data augmentation using GAN improved the coefficient of determination (R 2 ) by up to 7.02% and reduced the error of predictions by up to 4.29%. ANNs and CNNs benefited more from synthetic spectra than LGBMs, which only yielded slight improvement. All models showed optimal performance when 313 synthetic spectra were added to the original training data; further additions did not improve model performance because the quality of the datapoints generated by GAN beyond a certain threshold is poor, and one of the main reasons for this can be the size of the initial training data fed into the GAN. LGBMs showed superior performances than ANNs and CNNs on both the original and enhanced training datasets, which highlights the significance of selecting an appropriate machine learning or deep learning model for NIR spectral-data analysis. The results highlighted the positive impact of GAN on the predictive performance of models utilizing NIR spectroscopy as an NDE technique and monitoring tool for wood mechanical-property evaluation. Further studies should investigate the impact of the initial size of training data, the optimal number of generated synthetic spectra, and machine learning or deep learning models that could benefit more from data augmentation using GANs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Recurrent and convolutional neural networks for sequential multispectral optoacoustic tomography ( MSOT ) imaging

Abstract Multispectral optoacoustic tomography (MSOT) is a beneficial technique for diagnosing and analyzing biological samples since it provides meticulous details in anatomy and physiology. However, acquiring high through‐plane resolution volumetric MSOT is time‐consuming. Here, we propose a deep learning model based on hybrid recurrent and convolutional neural networks to generate sequential cross‐sectional images for an MSOT system. This system provides three modalities (MSOT, ultrasound, and optoacoustic imaging of a specific exogenous contrast agent) in a single scan. This study used ICG‐conjugated nanoworms particles (NWs‐ICG) as the contrast agent. Instead of acquiring seven images with a step size of 0.1 mm, we can receive two images with a step size of 0.6 mm as input for the proposed deep learning model. The deep learning model can generate five other images with a step size of 0.1 mm between these two input images meaning we can reduce acquisition time by approximately 71%.

Juhong, Aniwat↗
DOE OSTI2021

Boost of the Bio-memristor Performance for Artificial Electronic Synapses by Surface Reconstruction

Biomaterial-based memristors (bio-memristors) are often adopted to emulate biological synapse functions and applied to construct neural computing networks in brain-inspired chip systems. However, the randomness of conductive filament formation in bio-memristors inhibits their switching performance by causing the dispersion of the device-switching parameters. In this case, a facile porous silk fibroin (p-SF) memristor was obtained through a protein surface reconstruction strategy, in which the size of the hole can be adjusted by the density of hybrid nanoseeds. Here, the porous SF memristors exhibit greatly enhanced electrical characteristics, including uniform I-V cycles, centralized distribution of the switching voltages, and both high and low resistances, compared to devices without pores. The results of three-dimensional (3D) simulations based on classical density functional theory (cDFT) suggest that the reconstructed pores in the SF layers guide the formation and fracture of Ag filaments under an electric field and enhance the overall conductivity by separating Ag + ion and electron diffusion pathways. Ag + ions are predicted to preferentially diffuse through pores, whereas electrons diffuse through the SF network. Interestingly, the device conductance can be bidirectionally modulated gradually by positive and negative voltages, can faithfully simulate short-term and long-term plasticity, and can even realize the triplet-spike-timing-dependent plasticity (triplet-STDP) rule, which can be used for pattern recognition in biological systems. The simulation results reveal that a memristor network of this type has an accuracy of ~95.78% in memory learning and the capability of pattern learning. This work provides a facile technology route to improve the performance of bionic-material memristors.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Surrogate modeling of Cellular-Potts agent-based models as a segmentation task using the U-Net neural network architecture

The Cellular-Potts model is a powerful and ubiquitous framework for developing computational models for simulating complex multicellular biological systems. Cellular-Potts models (CPMs) are often computationally expensive due to the explicit modeling of interactions among large numbers of individual model agents and diffusive fields described by partial differential equations (PDEs). In this work, we develop a convolutional neural network (CNN) surrogate model using a U-Net architecture that accounts for periodic boundary conditions. We use this model to accelerate the evaluation of a mechanistic CPM previously used to investigate in vitro vasculogenesis. The surrogate model was trained to predict 100 computational steps ahead (Monte-Carlo steps, MCS), accelerating simulation evaluations by a factor of 562 times compared to single-core CPM code execution on CPU. Over short timescales of up to 3 recursive evaluations, or 300 MCS, our model captures the emergent behaviors demonstrated by the original Cellular-Potts model such as vessel sprouting, extension and anastomosis, and contraction of vascular lacunae. This approach demonstrates the potential for deep learning to serve as a step toward efficient surrogate models for CPM simulations, enabling faster evaluation of computationally expensive CPM simulations of biological processes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Towards a more general understanding of the algorithmic utility of recurrent connections

Lateral and recurrent connections are ubiquitous in biological neural circuits. Yet while the strong computational abilities of feedforward networks have been extensively studied, our understanding of the role and advantages of recurrent computations that might explain their prevalence remains an important open challenge. Foundational studies by Minsky and Roelfsema argued that computations that require propagation of global information for local computation to take place would particularly benefit from the sequential, parallel nature of processing in recurrent networks. Such “tag propagation” algorithms perform repeated, local propagation of information and were originally introduced in the context of detecting connectedness, a task that is challenging for feedforward networks. Here, we advance the understanding of the utility of lateral and recurrent computation by first performing a large-scale empirical study of neural architectures for the computation of connectedness to explore feedforward solutions more fully and establish robustly the importance of recurrent architectures. In addition, we highlight a tradeoff between computation time and performance and construct hybrid feedforward/recurrent models that perform well even in the presence of varying computational time limitations. We then generalize tag propagation architectures to propagating multiple interacting tags and demonstrate that these are efficient computational substrates for more general computations of connectedness by introducing and solving an abstracted biologically inspired decision-making task. Our work thus clarifies and expands the set of computational tasks that can be solved efficiently by recurrent computation, yielding hypotheses for structure in population activity that may be present in such tasks.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Integrated data-driven and experimental approaches to accelerate lead optimization targeting SARS-CoV- 2 main protease

Identification of potential therapeutic candidates can be expedited by integrating computational modeling with domain aware machine learning (ML) approaches followed by experimental validation. Generative deep learning models have been recently developed that can generate thousands of new candidates, but their physiochemical properties are typically not optimized. Using our deep learning models and a scaffold as a starting point, we generated tens of thousands of compounds for SARS-CoV-2 M pro that preserve the core scaffold. Here we utilized and implemented several computational tools such as structural alert and toxicity analysis, high throughput virtual screening, ML-based 3D quantitative structure–activity relationships, multi-parameter optimization, and graph neural networks on libraries of generated candidates to predict biological activity and binding affinity a priori. From these collective computational results, eight promising candidates were identified and tested experimentally using Native Mass Spectrometry (MS) and FRET-based functional assays. Two compounds, with quinazoline-2-thiol and acetylpiperidine core moiety showed IC 50 values in the low micromolar range: 2.95±0.0017 µM and 3.41±0.0015 µM, respectively. The molecular dynamics simulations further highlight that binding of these compounds results in allosteric modulations in the chain B and the interface domains of the M pro . The key fragments from these top hits can be used as input for closed loop lead optimization in the integrated pipeline.

60 APPLIED LIFE SCIENCES↗