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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 559 records · Page 31
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

AI-accelerated protein-ligand docking for SARS-CoV-2 is 100-fold faster with no significant change in detection

Protein-ligand docking is a computational method for identifying drug leads. The method is capable of narrowing a vast library of compounds down to a tractable size for downstream simulation or experimental testing and is widely used in drug discovery. While there has been progress in accelerating scoring of compounds with artificial intelligence, few works have bridged these successes back to the virtual screening community in terms of utility and forward-looking development. We demonstrate the power of high-speed ML models by scoring 1 billion molecules in under a day (50 k predictions per GPU seconds). We showcase a workflow for docking utilizing surrogate AI-based models as a pre-filter to a standard docking workflow. Our workflow is ten times faster at screening a library of compounds than the standard technique, with an error rate less than 0.01% of detecting the underlying best scoring 0.1% of compounds. Our analysis of the speedup explains that another order of magnitude speedup must come from model accuracy rather than computing speed. In order to drive another order of magnitude of acceleration, we share a benchmark dataset consisting of 200 million 3D complex structures and 2D structure scores across a consistent set of 13 million “in-stock” molecules over 15 receptors, or binding sites, across the SARS-CoV-2 proteome. We believe this is strong evidence for the community to begin focusing on improving the accuracy of surrogate models to improve the ability to screen massive compound libraries 100 × or even 1000 × faster than current techniques and reduce missing top hits. The technique outlined aims to be a fast drop-in replacement for docking for screening billion-scale molecular libraries.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Data-driven based coordinated smart inverter control for distributed energy resources

Smart inverters (SI) for distributed energy resources (DER) are becoming popular since they have the ability to stabilize as well as restore the voltage and frequency of power systems. Aiming at establishing the mathematical models combined with SI control methods, multiple optimization methods are developed. However, the computational complexity of solving such a mathematical model with various uncertainties limits the real-time application of the SI control. To conquer this challenge, a data-driven-based SI control approach is developed to achieve coordinated control in the high penetration DER system. First, an optimization problem for maximizing the active power generation and minimizing the power loss is designed using the Volt/VAR control. To reduce the time consumption, the recurrent neural network (RNN) is proposed to model the relationship between the uncertainties and control actions during the offline site. The RNN with different sub-structures such as the long short-term memory cell and gated recurrent unit cell are included to enrich the diversity of features. In the last stage, different experiment comparisons, including multiple uncertainties maps and stateof- art machine learning methods, are conducted to verify the effectiveness of the proposed method based on the IEEE 123 bus power system. The results demonstrate that the proposed method can effectively achieve a rapid and coordinated control with a lower error rate.

Qiu, Wei↗
DOE OSTI2025

Towards an Introspective Dynamic Model of Globally Distributed Computing Infrastructures

Large-scale scientific collaborations like ATLAS, Belle II, CMS, DUNE, and others involve hundreds of research institutes and thousands of researchers spread across the globe. These experiments generate petabytes of data, with volumes soon expected to reach exabytes. Consequently, there is a growing need for computation, including structured data processing from raw data to consumer-ready derived data, extensive Monte Carlo simulation campaigns, and a wide range of end-user analysis. To manage these computational and storage demands, centralized workflow and data management systems are implemented. However, decisions regarding data placement and payload allocation are often made disjointly and via heuristic means. A significant obstacle in adopting more effective heuristic or AI-driven solutions is the absence of a quick and reliable introspective dynamic model to evaluate and refine alternative approaches. In this study, we aim to develop such an interactive system using real-world data. By examining job execution records from the PanDA workflow management system, we have pinpointed key performance indicators such as queuing time, error rate, and the extent of remote data access. The dataset includes five months of activity. Additionally, we are creating a generative AI model to simulate time series of payloads, which incorporate visible features like category, event count, and submitting group, as well as hidden features like the total computational load—derived from existing PanDA records and computing site capabilities. These hidden features, which are not visible to job allocators, whether heuristic or AI-driven, influence factors such as queuing times and data movement.

kilic, Ozgur Ozan [Brookhaven National Laboratory ↗
DOE OSTI2014

Vector spin modeling for magnetic tunnel junctions with voltage dependent effects

Integration and co-design of CMOS and spin transfer devices requires accurate vector spin conduction modeling of magnetic tunnel junction (MTJ) devices. A physically realistic model of the MTJ should comprehend the spin torque dynamics of nanomagnet interacting with an injected vector spin current and the voltage dependent spin torque. Vector spin modeling allows for calculation of 3 component spin currents and potentials along with the charge currents/potentials in non-collinear magnetic systems. Here, we show 4-component vector spin conduction modeling of magnetic tunnel junction devices coupled with spin transfer torque in the nanomagnet. Nanomagnet dynamics, voltage dependent spin transport, and thermal noise are comprehended in a self-consistent fashion. We show comparison of the model with experimental magnetoresistance (MR) of MTJs and voltage degradation of MR with voltage. Proposed model enables MTJ circuit design that comprehends voltage dependent spin torque effects, switching error rates, spin degradation, and back hopping effects.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2014

Upper bounds on the error probabilities and asymptotic error exponents in quantum multiple state discrimination

We consider the multiple hypothesis testing problem for symmetric quantum state discrimination between r given states σ₁, …, σ{sub r}. By splitting up the overall test into multiple binary tests in various ways we obtain a number of upper bounds on the optimal error probability in terms of the binary error probabilities. These upper bounds allow us to deduce various bounds on the asymptotic error rate, for which it has been hypothesized that it is given by the multi-hypothesis quantum Chernoff bound (or Chernoff divergence) C(σ₁, …, σ{sub r}), as recently introduced by Nussbaum and Szkoła in analogy with Salikhov's classical multi-hypothesis Chernoff bound. This quantity is defined as the minimum of the pairwise binary Chernoff divergences min{sub j<k}C(σ{sub j},σ). It was known already that the optimal asymptotic rate must lie between C/3 and C, and that for certain classes of sets of states the bound is actually achieved. It was known to be achieved, in particular, when the state pair that is closest together in Chernoff divergence is more than 6 times closer than the next closest pair. Our results improve on this in two ways. First, we show that the optimal asymptotic rate must lie between C/2 and C. Second, we show that the Chernoff bound is already achieved when the closest state pair is more than 2 times closer than the next closest pair. We also show that the Chernoff bound is achieved when at least r - 2 of the states are pure, improving on a previous result by Nussbaum and Szkoła. Finally, we indicate a number of potential pathways along which a proof (or disproof) may eventually be found that the multi-hypothesis quantum Chernoff bound is always achieved.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Experimental multiplexing of quantum key distribution with classical optical communication

We demonstrate the realization of quantum key distribution (QKD) when combined with classical optical communication, and synchronous signals within a single optical fiber. In the experiment, the classical communication sources use Fabry-Pérot (FP) lasers, which are implemented extensively in optical access networks. To perform QKD, multistage band-stop filtering techniques are developed, and a wavelength-division multiplexing scheme is designed for the multi-longitudinal-mode FP lasers. We have managed to maintain sufficient isolation among the quantum channel, the synchronous channel and the classical channels to guarantee good QKD performance. Finally, the quantum bit error rate remains below a level of 2% across the entire practical application range. The proposed multiplexing scheme can ensure low classical light loss, and enables QKD over fiber lengths of up to 45 km simultaneously when the fibers are populated with bidirectional FP laser communications. Our demonstration paves the way for application of QKD to current optical access networks, where FP lasers are widely used by the end users.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Spinstand demonstration of areal density enhancement using two-dimensional magnetic recording (invited)

Exponential growth of the areal density has driven the magnetic recording industry for almost sixty years. But now areal density growth is slowing down, suggesting that current technologies are reaching their fundamental limit. The next generation of recording technologies, namely, energy-assisted writing and bit-patterned media, remains just over the horizon. Two-Dimensional Magnetic Recording (TDMR) is a promising new approach, enabling continued areal density growth with only modest changes to the heads and recording electronics. We demonstrate a first generation implementation of TDMR by using a dual-element read sensor to improve the recovery of data encoded by a conventional low-density parity-check (LDPC) channel. The signals are combined with a 2D equalizer into a single modified waveform that is decoded by a standard LDPC channel. Our detection hardware can perform simultaneous measurement of the pre- and post-combined error rate information, allowing one set of measurements to assess the absolute areal density capability of the TDMR system as well as the gain over a conventional shingled magnetic recording system with identical components. We discuss areal density measurements using this hardware and demonstrate gains exceeding five percent based on experimental dual reader components.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

A superconducting nanowire binary shift register

We present a design for a superconducting nanowire binary shift register, which stores digital states in the form of circulating supercurrents in high-kinetic-inductance loops. Adjacent superconducting loops are connected with nanocryotrons, three-terminal electrothermal switches, and fed with an alternating two-phase clock to synchronously transfer the digital state between the loops. A two-loop serial-input shift register was fabricated with thin-film NbN and a bit error rate of less than 10 –4 was achieved, when operated at a maximum clock frequency of 83 MHz and in an out-of-plane magnetic field of up to 6 mT. A shift register based on this technology offers an integrated solution for low-power readout of superconducting nanowire single photon detector arrays and is capable of interfacing directly with room-temperature electronics and operating unshielded in high magnetic field environments.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

A nanocryotron memory and logic family

The development of superconducting electronics based on nanocryotrons has been limited so far to few device circuits, in part due to the lack of standard and robust logic cells. Here, we introduce and experimentally demonstrate designs for a set of nanocryotron-based building blocks that can be configured and combined to implement memory and logic functions. The devices were fabricated by patterning a single superconducting layer of niobium nitride and measured in liquid helium on a wide range of operating points. The tests show 10 - 4 bit error rates with above ± 20 % margins up to 50 MHz and the possibility of operating under the effect of an out-of-plane 36 mT magnetic field, with ± 30 % margins at 10 MHz. Additionally, we designed and measured an equivalent delay-flip-flop made of two memory cells to show the possibility of combining multiple building blocks to make larger circuits. These blocks may constitute a solid foundation for the development of nanocryotron logic circuits and finite-state machines with potential applications in the integrated processing and control of superconducting nanowire single-photon detectors.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Fast and efficient Sb-based type-II phototransistors integrated on silicon

Increasing the energy efficiency and reducing the footprint of on-chip photodetectors enable dense optical interconnects for emerging computational and sensing applications. While heterojunction phototransistors (HPTs) exhibit high energy efficiency and negligible excess noise factor, their gain-bandwidth product (GBP) has been inferior to that of avalanche photodiodes at low optical powers. Here, we demonstrate that utilizing type-II energy band alignment in an Sb-based HPT results in six times smaller junction capacitance per unit area and a significantly higher GBP at low optical powers. These type-II HPTs were scaled down to 2 μm in diameter and fully integrated with photonic waveguides on silicon. Thanks to their extremely low dark current and high internal gain, these devices exhibit a GBP similar to the best avalanche devices (∼270 GHz) but with one order of magnitude better energy efficiency. Their energy consumption is about 5 fJ/bit at 3.2 Gbps, with an error rate below 10−9 at −25 dBm optical power at 1550 nm. These features suggest new opportunities for creating highly efficient and compact optical receivers based on phototransistors with type-II band alignment.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Dynamic in-context learning with conversational models for data extraction and materials property prediction

The advent of natural language processing and large language models (LLMs) has revolutionized the extraction of data from unstructured scholarly papers. However, ensuring data trustworthiness remains a significant challenge. In this paper, we introduce PropertyExtractor, an open-source tool that leverages advanced conversational LLMs such as Google gemini-pro and OpenAI gpt-4, blends zero-shot with few-shot in-context learning, and employs engineered prompts for the dynamic refinement of structured information hierarchies—enabling autonomous, efficient, scalable, and accurate identification, extraction, and verification of material property data. Our tests on material data demonstrate precision and recall that exceed 95% with an error rate of ∼9%, highlighting the effectiveness and versatility of the toolkit. Finally, databases for 2D material thicknesses, a critical parameter for device integration, and energy bandgap values are developed using PropertyExtractor. In particular, for the thickness database, the rapid evolution of the field has outpaced both experimental measurements and computational methods, creating a significant data gap. Our work addresses this gap and showcases the potential of PropertyExtractor as a reliable and efficient tool for the autonomous generation of various material property databases, advancing the field.

Ekuma, Chinedu E. (ORCID:0000000258527556)↗
DOE OSTI2017

Study of nonlinearities in the transmission of a QAM signal in fibre-optic communication lines using different carrier pulses

We study the transmission of a quadrature amplitude modulation signal over a fibre-optic communication line for different carrier pulses forming a signal. It is shown that in using ‘raised cosine’ and ‘root raised cosine’ pulses, it is possible to further reduce the bit error rate by an order of magnitude with the help of a special modulation format. (paper)

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

National population mapping from sparse survey data: A hierarchical Bayesian modeling framework to account for uncertainty

Population estimates are critical for government services, development projects, and public health campaigns. Such data are typically obtained through a national population and housing census. However, population estimates can quickly become inaccurate in localized areas, particularly where migration or displacement has occurred. Some conflict-affected and resource-poor countries have not conducted a census in over 10 y. We developed a hierarchical Bayesian model to estimate population numbers in small areas based on enumeration data from sample areas and nationwide information about administrative boundaries, building locations, settlement types, and other factors related to population density. We demonstrated this model by estimating population sizes in every 10- m grid cell in Nigeria with national coverage. These gridded population estimates and areal population totals derived from them are accompanied by estimates of uncertainty based on Bayesian posterior probabilities. The model had an overall error rate of 67 people per hectare (mean of absolute residuals) or 43% (using scaled residuals) for predictions in out-of-sample survey areas (approximately 3 ha each), with increased precision expected for aggregated population totals in larger areas. This statistical approach represents a significant step toward estimating populations at high resolution with national coverage in the absence of a complete and recent census, while also providing reliable estimates of uncertainty to support informed decision making.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

The Octopus processor for the CMS L1 muon trigger for High Luminosity LHC

The upgraded L1 muon trigger system of the CMS experiment in the High Luminosity Large Hadron Collider is based on custom processors featuring large Field Programmable Gate Arrays (FPGAs) connected by large numbers of optical links. These provide the I/O bandwidth and power necessary to process the complex algorithms used during the collection of physics data. The design and performance requirements of these processors creates significant challenges in signal integrity, power delivery, and thermal management. In this paper we describe the Octopus processor, featuring a large Xilinx Virtex Ultrascale+ FPGA and up to 128 links interfaced to optics through high quality twin-ax copper cables. Results on signal integrity at 25 Gb/s and the first demonstration of 50+ Gb/s links with pluggable optics in CMS are also shown, demonstrating bit error rates below 10 –15 at a 95% confidence level. The thermal performance is measured inside an Advanced-TCA crate with acceptable thermal margins up to 200 W of chip power. Future improvements are mentioned, potentially allowing operation at up to 300 W.

Instruments & Instrumentation↗
DOE OSTI2025

Development of a test system for data links of the ATLAS Inner Tracker (ITk) upgrade silicon pixel detector

This contribution introduces a novel test system developed to evaluate the signal transmission quality in high-speed data links for the 2026 Inner Tracker (ITk) upgrade of the ATLAS experiment. Using an FPGA-based data acquisition (DAQ) framework, the setup can run simultaneous Bit Error Rate (BER) tests for up to 64 channels and generate virtual eye diagrams, for qualifying the ∼26k electrical links at the ATLAS ITk data rate of 1.28 Gb/s. The paper includes results from system calibration, yielding its contribution to the measured losses, and preliminary results from tests of prototype and pre-production assemblies of on-detector links of the three ATLAS ITk Pixel subsystems.

47 OTHER INSTRUMENTATION↗
DOE OSTI2019

Towards quantum machine learning with tensor networks

Machine learning is a promising application of quantum computing, but challenges remain for implementation today because near-term devices have a limited number of physical qubits and high error rates. Motivated by the usefulness of tensor networks for machine learning in the classical context, we propose quantum computing approaches to both discriminative and generative learning, with circuits based on tree and matrix product state tensor networks, that could already have benefits with such near-term devices. The result is a unified framework in which classical and quantum computing can benefit from the same theoretical and algorithmic developments, and the same model can be trained classically then transferred to the quantum setting for additional optimization. Tensor network circuits can also provide qubit-efficient schemes in which, depending on the architecture, the number of physical qubits required scales only logarithmically with, or independently of the input or output data sizes. Here, we demonstrate our proposals with numerical experiments, training a discriminative model to perform handwriting recognition using a hybrid quantum-classical optimization procedure that could be carried out on quantum hardware today, and testing the noise resilience of the trained model.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Domain-specific compilers for dynamic simulations of quantum materials on quantum computers

Abstract Simulation of the dynamics of quantum materials is emerging as a promising scientific application for noisy intermediate-scale quantum (NISQ) computers. Due to their high gate-error rates and short decoherence times, however, NISQ computers can only produce high-fidelity results for those quantum circuits smaller than some given circuit size. Dynamic simulations, therefore, pose a challenge as current algorithms produce circuits that grow in size with each subsequent time-step of the simulation. This underscores the crucial role of quantum circuit compilers to produce executable quantum circuits of minimal size, thereby maximizing the range of physical phenomena that can be studied within the NISQ fidelity budget. Here, we present two domain-specific (DS) quantum circuit compilers for the Rigetti and IBM quantum computers, specifically designed to compile circuits simulating dynamics under a special class of time-dependent Hamiltonians. The compilers outperform state-of-the-art general-purpose compilers in terms of circuit size reduction by around 25%–30% as well as wall-clock compilation time by around 40% (dependent on system size and simulation time-step). Drawing on heuristic techniques commonly used in artificial intelligence, both compilers scale well with simulation time-step and system size. Code for both compilers is open-source and packaged into a full-stack quantum simulation software with tutorials included for ease of use for future researchers wishing to perform dynamic simulations of quantum materials on quantum computers. As our DS compilers provide significant improvements in both compilation time and simulation fidelity, they provide a building block for accelerating progress toward physical quantum supremacy.

Physics↗