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At least 235 records · Page 13

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DOE OSTI2020

Combinatorial Study of MZO Emitters for CdTe-Based Solar Cells

Precise band alignment at the front interface of CdTe-based solar cells is necessary to reduce photoexcited charge carrier recombination and to enable higher energy conversion efficiencies. Magnesium zinc oxide is not only transparent but also offers the ability to tune the conduction band offset with CdTe at the front of the device stack. In this work, we describe reactive sputtering of Mg 1-x Zn x O using metal targets and its application in CdTe photovoltaics. Combinatorial libraries of Mg 1-x Zn x O were created to tune the conduction band alignment for the optimal performance of CdTe solar cells. Band gap variation of more than 0.4 eV is achieved across a 3-inch substrate. Kelvin probe measurements demonstrate variance of work function across the substrate. Device performance is optimized at band gaps of 3.5 eV for the CdTe device processing conditions employed in this study.

14 SOLAR ENERGY↗

DOE OSTI2020

Combinatorial-Hierarchical DNA Library Design Using the TeselaGen DESIGN Module with j5.

Modern DNA assembly techniques are known for their potential to link multiple large DNA fragments together into even larger constructs in single pot reactions that are easier to automate and work more reliably than traditional cloning methods. The simplicity of the chemistry is in contrast to the increased work needed to design optimal reactions that maximize DNA fragment reuse, minimize cost, and organize thousands of potential chemical reactions. Here we examine available DNA assembly methods and describe through example, the construction of a complex but not atypical combinatorial and hierarchical library using protocols that are generated automatically with the assistance of modern synthetic biology software.

Fero, Michael J↗

NASA NTRS1980

Test vectors development and optimization for a microprocessor

This paper describes a method for generating and optimizing test vectors for a microprocessor, with the aid of a fault simulator implemented entirely by hardware. The development and optimization of test vectors has been done on a tester, with the fault simulator plugged directly into the test head. The fault simulator is capable of automatically injecting over a thousand single or multiple stuck faults in the sequential and combinatorial parts of the microprocessor. The test vectors developed by a programmer working interactively with the tester were applied through the tester to the fault simulator, and the percent of faults detected was measured. The vectors were developed and optimized for the 1802 microprocessor, with the objective of detecting 100% of the single stuck faults with a minimum set of vectors. Experimental results show that 99.7% of the single stuck faults are being detected with approximately 14,000 vectors.

Timoc, C. C.↗

DOE OSTI2025

A Review of Quantum Computing Technologies in Power System Optimization

As modern power grids increasingly integrate variable renewable generation, distributed energy resources, and energy storage systems, classical optimization techniques are facing unprecedented challenges. This review examines the emerging application of quantum computing to overcome these challenges in power system optimization, including optimal power flow (OPF), unit commitment (UC), economic dispatch (ED), and intelligent switching and topology optimization (IS-TO). Recent research has introduced various quantum methodologies—such as gate-based, annealing-based, variational algorithms, and quantum-inspired algorithms—to address the combinatorial complexity inherent in grid reconfiguration and energy management. The review summaries the quantum algorithms, quantum devices and the power system test cases, highlighting hybrid quantum–classical strategies that leverage the complementary strengths of both paradigms. Some quantum advantages have been observed, including theoretical speedup, accurate simulation results, scalable qubit usage, efficient QUBO mapping. In particular, the review emphasizes the importance of integrating quantum optimization techniques with classical control frameworks, these hybrid approaches demonstrate the potential to improve real-time grid management and operational reliability. A significant portion of the analysis is devoted to the practical limitations of current quantum devices. Present-day quantum hardware, operating in the noisy intermediate-scale quantum (NISQ) era, remains highly sensitive to noise and limited in qubit connectivity, which constrains the scale and accuracy of implemented algorithms. The review delves into specific challenges such as the need for qubit-efficient encoding techniques and error mitigation strategies that are critical for handling real-world grid optimization problems. In addition, the work draws attention to the performance discrepancies between theoretical quantum speedups and experimental validations, underscoring the importance of rigorous benchmark studies using representative power grid test cases. In summary, this review highlights both the promise and limitations of quantum computing for power system optimization. It provides a comprehensive overview of the state-of-the-art technologies, categorizes recent advancements in algorithm design, and discusses practical considerations for implementation, and serves as an informative resource on current research. Future research directions include developing robust hybrid frameworks, advancing qubit-efficient formulations, and scaling up experimental demonstrations to confirm the theoretical advantages of quantum methods in large-scale power system operations.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

Domain Decomposition for Integer Optimal Control with Total Variation Regularization

Total variation integer optimal control problems admit solutions and necessary optimality conditions via geometric variational analysis. In spite of the existence of said solutions, algorithms which solve the discretized objective suffer from high numerical cost associated with the combinatorial nature of integer programming. Hence, such methods are often limited to small and medium-sized problems. We propose a globally convergent, coordinate descent–inspired algorithm that allows tractable subproblem solutions restricted to a partition of the domain. Our decomposition method solves relatively small trust-region subproblems that modify the control variable on a subdomain only. Given nontrivial subdomain overlap, we prove that a global first-order necessary optimality condition is equivalent to a first-order necessary optimality condition per subdomain. We additionally show that a sufficient decrease is achieved on a single subdomain by way of a trust-region subproblem solver using geometric measure–theoretic arguments, which we integrate with a greedy patch selection to prove convergence of our algorithm. In conclusion, we demonstrate the practicality of our algorithm on a benchmark large-scale, PDE-constrained integer optimal control problem and find that our method is faster than the state of the art.

domain decomposition↗

DOE OSTI2022

High throughput discovery of enhanced visible photoactivity in Fe–Cr vanadate solar fuels photoanodes

Metal oxide solar absorbers are well suited for photoelectrochemical applications where requisite properties include stability in highly oxidizing environments, in addition to solar energy conversion. Metal vanadates are of particular interest due to their relatively low band gap energies compared to traditional, wide-gap photocatalysts. Concerted efforts on BiVO 4 -based photoanodes have revealed multiple avenues for improving the solar conversion efficiencies for photon energies above 2.5 eV but have not addressed the ultimate performance limitations from the undesirably high band gap energy. Fe and Cr vanadates have a lower band gap and thus a higher potential solar conversion efficiency, although to-date the absorbed 2–2.5 eV photons are not effectively converted to the desired anodic photocurrent. By using combinatorial synthesis and high throughput screening, we demonstrate that cation substitutions with the monoclinic MVO 4 phase (M = Cr, Fe) improves the utilization of photons in this energy range. Given the portfolio of photoanode improvement techniques available, we suggest optimization of (Cr 0.5 Fe 0.5 )VO 4 -based photoanodes as a promising path for enable solar fuel technologies.

14 SOLAR ENERGY↗

DOE OSTI2020

Quantum computing based hybrid solution strategies for large-scale discrete-continuous optimization problems

Quantum computing (QC) has gained popularity due to its unique capabilities that are quite different from that of classical computers in terms of speed and methods of operations. This paper proposes hybrid models and methods that effectively leverage the complementary strengths of deterministic algorithms and QC techniques to overcome combinatorial complexity for solving large-scale mixed-integer programming problems. Four applications, namely the molecular conformation problem, job-shop scheduling problem, manufacturing cell formation problem, and the vehicle routing problem, are specifically addressed. Large-scale instances of these application problems across multiple scales ranging from molecular design to logistics optimization are computationally challenging for deterministic optimization algorithms on classical computers. To address the computational challenges, hybrid QC-based algorithms are proposed and extensive computational experimental results are presented to demonstrate their applicability and efficiency. The proposed QC-based solution strategies enjoy high computational efficiency in terms of solution quality and computation time, by utilizing the unique features of both classical and quantum computers.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Enabling Modular Autonomous Feedback‐Loops in Materials Science through Hierarchical Experimental Laboratory Automation and Orchestration

Abstract Materials acceleration platforms (MAPs) operate on the paradigm of integrating combinatorial synthesis, high‐throughput characterization, automatic analysis, and machine learning. Within a MAP, one or multiple autonomous feedback loops may aim to optimize materials for certain functional properties or to generate new insights. The scope of a given experiment campaign is defined by the range of experiment and analysis actions that are integrated into the experiment framework. Herein, the authors present a method for integrating many actions within a hierarchical experimental laboratory automation and orchestration (HELAO) framework. They demonstrate the capability of orchestrating distributed research instruments that can incorporate data from experiments, simulations, and databases. HELAO interfaces laboratory hardware and software distributed across several computers and operating systems for executing experiments, data analysis, provenance tracking, and autonomous planning. Parallelization is an effective approach for accelerating knowledge generation provided that multiple instruments can be effectively coordinated, which the authors demonstrate with parallel electrochemistry experiments orchestrated by HELAO. Efficient implementation of autonomous research strategies requires device sharing, asynchronous multithreading, and full integration of data management in experimental orchestration, which to the best of the authors’ knowledge, is demonstrated for the first time herein.

36 MATERIALS SCIENCE↗

NASA NTRS1997

Optimizing an Actuator Array for the Control of Multi-Frequency Noise in Aircraft Interiors

Techniques developed for selecting an optimized actuator array for interior noise reduction at a single frequency are extended to the multi-frequency case. Transfer functions for 64 actuators were obtained at 5 frequencies from ground testing the rear section of a fully trimmed DC-9 fuselage. A single loudspeaker facing the left side of the aircraft was the primary source. A combinatorial search procedure (tabu search) was employed to find optimum actuator subsets of from 2 to 16 actuators. Noise reduction predictions derived from the transfer functions were used as a basis for evaluating actuator subsets during optimization. Results indicate that it is necessary to constrain actuator forces during optimization. Unconstrained optimizations selected actuators which require unrealistically large forces. Two methods of constraint are evaluated. It is shown that a fast, but approximate, method yields results equivalent to an accurate, but computationally expensive, method.

Palumbo, D. L.↗

DOE OSTI2022

High-throughput investigation of structural evolution upon solid-state in Cu–Cr–Co combinatorial multilayer thin-film

Cu-Cr-Co combinatorial multilayer thin-films were prepared by a high-throughput ion beam sputtering system. Based on the thickness ratio among the individual nanoscale monolayers (Cu, Cr, Co), the resulting stoichiometry covered the entire phase diagram. The chemical composition and structure of Cu-Cr-Co combinatorial chip upon solid-state reaction were studied by lab-based micro-X-ray fluorescence (mu-XRF) and high-throughput synchrotron X-ray diffraction (XRD), respectively. A composition-structure map for Cu-Cr-Co combinatorial chip was developed through automated data analysis employing hierarchical clustering techniques. The structural evolution of Cu-Cr-Co combinatorial chip as a function of heat-treatment temperature, time, and modulation period were studied systematically. Furthermore, the effect of the elemental distribution in-depth direction was investigated to gain more insights regarding phase transformation. This work provides an efficient method and new perspectives for the design and optimization of the composition and structure of high-performance thin-films.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Computational design of high-entropy rare earth aluminum garnets for advanced thermal and environmental barrier coatings

To enhance the protection of Ni-based superalloys in gas turbine engines’ high-temperature environments, it’s crucial to develop advanced thermal/environmental barrier coating (T/EBC) materials with a balanced combination of thermal and mechanical properties. This optimization is essential to safeguard against chemical and thermal challenges. Here, in this study, we harness the power of density functional theory (DFT) in conjunction with combinatorial chemistry methodologies to engineer high-performance high-entropy rare earth disilicates of the RE 3 Al 5 O 12 family (where RE denotes Y, Gd, Er, and Yb). These materials are meticulously designed to exhibit superior phase stability, a targeted coefficient of thermal expansion (CTE), low lattice thermal conductivity, and robust mechanical properties. The determination of CTE values is accomplished through phonon calculations at various volume settings within the quasi-harmonic approximation, while lattice thermal conductivities are rigorously assessed employing the Debye-Callaway model, accounting for three distinct phonon processes. Our findings highlight the remarkable attributes of the solid solution (Y 1/4 Gd 1/4 Er 1/4 Yb 1/4 ) 3 Al 5 O 12 , which displays a reduction in lattice thermal conductivity compared to its individual constituents while maintaining a favorable range of CTE values. The novel T/EBC material, distinguished by their multifaceted functionalities, are poised to use in substantial enhancements in the performance of engines.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Activity profiling and crystal structures of inhibitor-bound SARS-CoV-2 papain-like protease: A framework for anti–COVID-19 drug design

Viral papain-like cysteine protease (PLpro, NSP3) is essential for SARS-CoV-2 replication and represents a promising target for the development of antiviral drugs. Here, we used a combinatorial substrate library and performed comprehensive activity profiling of SARS-CoV-2 PLpro. On the scaffold of the best hits from positional scanning, we designed optimal fluorogenic substrates and irreversible inhibitors with a high degree of selectivity for SARS PLpro. We determined crystal structures of two of these inhibitors in complex with SARS-CoV-2 PLpro that reveals their inhibitory mechanisms and provides a molecular basis for the observed substrate specificity profiles. Last, we demonstrate that SARS-CoV-2 PLpro harbors deISGylating activity similar to SARSCoV-1 PLpro but its ability to hydrolyze K48-linked Ub chains is diminished, which our sequence and structure analysis provides a basis for. Together, this work has revealed the molecular rules governing PLpro substrate specificity and provides a framework for development of inhibitors with potential therapeutic value or drug repurposing.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

An evolutionary algorithm for designing microbial communities via environmental modification

Despite a growing understanding of how environmental composition affects microbial communities, it remains difficult to apply this knowledge to the rational design of synthetic multispecies consortia. This is because natural microbial communities can harbour thousands of different organisms and environmental substrates, making up a vast combinatorial space that precludes exhaustive experimental testing and computational prediction. Here, we present a method based on the combination of machine learning and metabolic modelling that selects optimal environmental compositions to produce target community phenotypes. In this framework, dynamic flux balance analysis is used to model the growth of a community in candidate environments. A genetic algorithm is then used to evaluate the behaviour of the community relative to a target phenotype, and subsequently adjust the environment to allow the organisms to approach this target. We apply this iterative process to thousands of in silico communities of varying sizes, showing how it can rapidly identify environments that yield desired taxonomic compositions and patterns of metabolic exchange. Moreover, this combination of approaches produces testable predictions for the assembly of experimental microbial communities with specific properties and can facilitate rational environmental design processes for complex microbiomes.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2017

Initial Results of Heuristic Guided Orbit Selection for a Low Frequency Radio Interferometric Spacecraft Constellation

A constellation of radio telescope spacecraft can leverage interferometry to accurately image distant objects throughout the universe, but mission design must balance among many interrelated constraints. In particular, the number of craft and the selection of time-varying orbital parameters play a pivotal role in determining what interferometric baselines are feasible with respect to different targets, and thus drives the breadth and quality of data available to the constellation. The large combinatorial orbit configuration space and competing concerns present a challenging problem that is not well addressed by traditional mission design processes. This paper describes application of automated optimization methods to help direct mission design effort to the most promising dynamic constellation geometries: those that achieve broad interferometric coverage but remain cost-effective and resilient to failures. Several automatic heuristic-driven optimization algorithms representing complementary search strategies were created to explore among concrete constellation configuration plans. Evaluation of each candidate constellation plan was accelerated by efficiently combining precomputed caches of orbital and interferometric data. Results indicate that leveraging automated optimization for constellation mission design is both practical and illuminating: generated solutions provided both evidence for existing design intuitions as well as fresh insights into novel configurations.

Hernandez, Sonia↗

DOE OSTI2022

An Online Approach to Solve the Dynamic Vehicle Routing Problem with Stochastic Trip Requests for Paratransit Services

Many transit agencies operating paratransit and microtransit services have to respond to trip requests that arrive in real-time, which entails solving hard combinatorial and sequential decision-making problems under uncertainty. To avoid decisions that lead to significant inefficiency in the long term, vehicles should be allocated to requests by optimizing a non-myopic utility function or by batching requests together and optimizing a myopic utility function. While the former approach is typically offline, the latter can be performed online. We point out two major issues with such approaches when applied to paratransit services in practice. First, it is difficult to batch paratransit requests together as they are temporally sparse. Second, the environment in which transit agencies operate changes dynamically (e.g., traffic conditions can change over time), causing the estimates that are learned offline to become stale. To address these challenges, we propose a fully online approach to solve the dynamic vehicle routing problem (DVRP) with time windows and stochastic trip requests that is robust to changing environmental dynamics by construction. We focus on scenarios where requests are relatively sparse—our problem is motivated by applications to paratransit services. We formulate DVRP as a Markov decision process and use Monte Carlo tree search to evaluate actions for any given state. Accounting for stochastic requests while optimizing a non-myopic utility function is computationally challenging; indeed, the action space for such a problem is intractably large in practice. To tackle the large action space, we leverage the structure of the problem to design heuristics that can sample promising actions for the tree search. Our experiments using real-world data from our partner agency show that the proposed approach outperforms existing state-of-the-art approaches both in terms of performance and robustness.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

DIVA/DeviceEditor (DIVA) v6.0.0

The DIVA software interfaces a process in which researchers design their DNA with a web-based graphical user interface (DeviceEditor), submit their designs to a central queue, and a few weeks later receive their sequence-verified clonal constructs. Each researcher independently designs the DNA to be constructed with a web-based BioCAD tool, and presses a button to submit their designs to a central queue. Researchers have web-based access to their DNA design queues, and can track the progress of their submitted designs as they progress from "evaluation", to "waiting for reagents", to "in progress", to "complete". Researchers access their completed constructs through the central DNA repository. Along the way, all DNA construction success/failure rates are captured in a central database. Once a design has been submitted to the queue, a small number of dedicated staff evaluate the design for feasibility and provide feedback to the responsible researcher if the design is either unreasonable (e.g., encompasses a combinatorial library of a billion constructs) or small design changes could significantly facilitate the downstream implementation process. The dedicated staff then use DNA assembly design automation software to optimize the DNA construction process for the design, leveraging existing parts from the DNA repository where possible and ordering synthetic DNA where necessary. Once all requisite process inputs are available, the design progresses from "waiting for reagents" to "in progress" in the design queue. Human-readable and machine-parseable DNA construction protocols output by the DNA assembly design automation software are then executed by the dedicated staff exploiting lab automation devices wherever possible. Since the all employed DNA construction methods are sequence-agnostic, standardized (utilize the same enzymatic master mixes and reaction conditions), completely independent DNA construction tasks can be aggregated into the same multi-well plates and pursued in parallel. The resulting sets of cloned constructs can then be screened by high-throughput next-gen sequencing platforms for sequence correctness. A combination of long read-length (e.g., PacBio) and paired-end read platforms (e.g., Illumina) would be exploited depending the particular task at hand (e.g., PacBio might be sufficient to screen a set of pooled constructs with significant gene divergence). Post sequence verification, designs for which at least one correct clone was identified will progress to a "complete" status, while designs for which no correct clones were identified will progress to a "failure" status. Depending on the failure mode (e.g., no transformants), and how many prior attempts/variations of assembly protocol have been already made for a given design, subsequent attempts may be made or the design can progress to a "permanent failure" state. All success and failure rate information will be captured during the process, including at which stage a given clonal construction procedure failed (e.g., no PCR product) and what the exact failure was (e.g. assembly piece 2 missing). This success/failure rate data can be leveraged to refine the DNA assembly design process.

Plahar, Hector↗

NASA NTRS1988

A prototype combinatorial scheduler for space-borne observational instruments

A prototype scheduler has been developed for observations to be carried out by the NASA Gamma Ray Observatory. The purpose of the scheduling system is to provide an automated method to determine optimized schedules conforming to all scheduling guidelines. Strategies used in creating COMPTEL/EGRET schedules and in scheduling OSSE targets are examined. It is concluded that the hybrid scheduler creates optimal schedules, or very nearly optimal schedules, and reduces the estimated scheduling time by 18 or 19 orders of magnitude, from about 10 to the 14th years to as few as 561 seconds.

Gilstrap, Lewey O.↗

NASA NTRS2011

Optimizing Muscle Parameters in Musculoskeletal Modeling Using Monte Carlo Simulations

Astronauts assigned to long-duration missions experience bone and muscle atrophy in the lower limbs. The use of musculoskeletal simulation software has become a useful tool for modeling joint and muscle forces during human activity in reduced gravity as access to direct experimentation is limited. Knowledge of muscle and joint loads can better inform the design of exercise protocols and exercise countermeasure equipment. In this study, the LifeModeler(TM) (San Clemente, CA) biomechanics simulation software was used to model a squat exercise. The initial model using default parameters yielded physiologically reasonable hip-joint forces. However, no activation was predicted in some large muscles such as rectus femoris, which have been shown to be active in 1-g performance of the activity. Parametric testing was conducted using Monte Carlo methods and combinatorial reduction to find a muscle parameter set that more closely matched physiologically observed activation patterns during the squat exercise. Peak hip joint force using the default parameters was 2.96 times body weight (BW) and increased to 3.21 BW in an optimized, feature-selected test case. The rectus femoris was predicted to peak at 60.1% activation following muscle recruitment optimization, compared to 19.2% activation with default parameters. These results indicate the critical role that muscle parameters play in joint force estimation and the need for exploration of the solution space to achieve physiologically realistic muscle activation.

Hanson, Andrea↗