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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

Deposition and characterization of molybdenum thin films using dc-plasma magnetron sputtering

Molebdenum (Mo) thin films were deposited on well-cleaned soda-lime glass substrates using DC-plasma magnetron sputtering. In the design of experiment deposition was optimized for maximum beneficial characteristics by monitoring effect of process variables such as deposition power (100–200 W). Their electrical, structural and morphological properties were analyzed to study the effect of these variables. The electrical resistivity of Mo thin films could be reduced by increasing deposition power. Within the range of analyzed deposition power, Mo thin films showed a mono crystalline nature and the crystallites were found to have an orientation along [110] direction. The surface morphology of thin films showed that a highly dense micro structure has been obtained. The surface roughness of films increased with deposition power. The adhesion of Mo thin films could be improved by increasing the deposition power. Atomic force microscopy was used for the topographical study of the films and to determine the roughness of the films. X-ray diffractrometer and scanning electron microscopy analysis were used to investigate the crystallinity and surface morphology of the films. Hall effect measurement system was used to find resistivity, carrier mobility and carrier density of deposited films. The adhesion test was performed using scotch hatch tape adhesion test. Mo thin films prepared at deposition power of 200 W, substrate temperature of 23°C and Ar pressure of 0.0123 mbar exhibited a mono crystalline structure with an orientation along (110) direction, thickness of ∼550 nm and electrical resistivity value of 0.57 × 10{sup −4} Ω cm.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Fast neutron background characterization of the future Ricochet experiment at the ILL research nuclear reactor

Abstract The future Ricochet experiment aims at searching for new physics in the electroweak sector by providing a high precision measurement of the Coherent Elastic Neutrino-Nucleus Scattering (CENNS) process down to the sub-100 eV nuclear recoil energy range. The experiment will deploy a kg-scale low-energy-threshold detector array combining Ge and Zn target crystals 8.8 m away from the 58 MW research nuclear reactor core of the Institut Laue Langevin (ILL) in Grenoble, France. Currently, the Ricochet Collaboration is characterizing the backgrounds at its future experimental site in order to optimize the experiment’s shielding design. The most threatening background component, which cannot be actively rejected by particle identification, consists of keV-scale neutron-induced nuclear recoils. These initial fast neutrons are generated by the reactor core and surrounding experiments (reactogenics), and by the cosmic rays producing primary neutrons and muon-induced neutrons in the surrounding materials. In this paper, we present the Ricochet neutron background characterization using $$^3$$ 3 He proportional counters which exhibit a high sensitivity to thermal, epithermal and fast neutrons. We compare these measurements to the Ricochet Geant4 simulations to validate our reactogenic and cosmogenic neutron background estimations. Eventually, we present our estimated neutron background for the future Ricochet experiment and the resulting CENNS detection significance. Our results show that depending on the effectiveness of the muon veto, we expect a total nuclear recoil background rate between 44 ± 3 and 9 ± 2 events/day/kg in the CENNS region of interest, i.e. between 50 eV and 1 keV. We therefore found that the Ricochet experiment should reach a statistical significance of 4.6 to 13.6 $$\sigma $$ σ for the detection of CENNS after one reactor cycle, when only the limiting neutron background is considered.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2015

Hydrogen Selective Exfoliated Zeolite Membranes

The objective of this project was to develop and evaluate an innovative membrane technology at process conditions that would be representative of Integrated Gasification Combined Cycle (IGCC) advanced power generation with pre-combustion capture of carbon dioxide (CO 2 ). This research focused on hydrogen (H 2 )-selective zeolite membranes that could be utilized to separate conditioned syngas into H 2 -rich and CO 2 -rich components. Both experiments and process design and optimization calculations were performed to evaluate the concept of ultra-thin membranes made from zeolites nanosheets. In this work, efforts in the laboratory were made to tackle two fundamental challenges in application of zeolite membranes in harsh industrial environments, namely, membrane thickness and membrane stability. Conventional zeolite membranes have thicknesses in the micron range, limiting their performance. In this research, we developed a method for fabrication of ultimately thin zeolite membranes based on zeolite nanosheets. A range of layered zeolites (MWW, RWR, NSI structure types) suitable for hydrogen separation was successfully exfoliated to their constituent nanosheets. Further, membranes were made from one of these zeolites, MWW, to demonstrate the potential of this group of materials. Moreover, long-term steam stability of these zeolites (up to 6 months) was investigated in high concentrations of steam (35 mol% and 95 mole%), high pressure (10 barg), and high temperatures (350 °C and 600 °C) relevant to conditions of water-gas-shift and steam methane reforming reactions. It was found that certain nanosheets are stable, and that stability depends on the concentration of structural defects. Additionally, models that represent a water-gas-shift (WGS) membrane reactor equipped with the zeolite membrane were developed for systems studies. These studies had the aim of analyzing the effect of the membrane reactor integration into IGCC plants in terms of performance and economic aspects of the plants. Specifically, simulation and design optimization studies were performed using the developed stand-alone membrane reactor models to identify the membrane selectivity and permeance characteristics necessary to achieve desired targets of CO 2 capture and H 2 recovery, as well as guide the selection of the optimal reactor design that minimizes the membrane cost as a function of its surface area required. The isothermal membrane reactor model was also integrated into IGCC system models using both the MATLAB and Aspen software platforms and techno-economic analyses of the integrated plants have been carried out to evaluate the feasibility of replacing current technologies for pre-combustion capture by the proposed novel approach in terms of satisfying stream constraints and achieving the DOE target goal of 90% CO 2 capture. The results of the performed analyses based on present value of annuity calculations showed break even costs for the membrane reactor within the feasible range for membrane fabrication. However, the predicted membrane performance used in these simulations exceeded the performance achieved experimentally. Therefore, further work is required to improve membrane performance.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2018

High Air-Flow Aerosol Filtration Testing

Pressure losses and aerosol collection efficiencies were measured for fibrous filter materials at air-flow rates consistent with high efficiency filtration (hundreds of cubic feet per minute). Microfiber filters coated with nanofibers were purchased and fabricated into test assemblies for a 12-inch duct system designed to mimic high efficiency filtration testing of commercial and industrial processes. Standards and specifications for high efficiency filtration were studied from a variety of institutions to assess protocols for design, testing, operations and maintenance, and quality assurance (e.g., DOE, ASHRAE, ASME). Three materials with varying Minimum Efficiency Reporting Values (MERV) were challenged with sodium chloride aerosol. Substantial filter loading was observed where aerosol collection efficiencies and pressure losses increased during experiments. Filter designs will be optimized and characterized in subsequent years of this study. Additional testing will be performed with higher hazard aerosols at Oak Ridge National Laboratory.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2019

DiaMonD: An Integrated Multifaceted Approach to Mathematics at the Interfaces of Data, Models, and Decisions

For many problems in computational science and engineering, the so-called forward problem—solution of the underlying mathematical model to yield output quantities of interest, given input parameters—is difficult enough for frontier complex models, which are often characterized by coupled multiphysics and possibly stochastic behavior over a wide range of length and time scales. However, we face the need to move beyond the forward problem, to address what is often the ultimate goal of CS&E: decision making. This requires us to tackle a spectrum of mathematical problems that subsume, and are thus even more difficult than, the forward problem. First, given experimental data, we wish to estimate unknown parameters characterizing a model of a natural or engineered system by solving an inverse problem. Second, we seek the optimal configuration of the system (or experiments) by solving an optimal design problem. Third, the optimal operation of the system must be determined by solving an optimal control problem. And fourth, we must quantify uncertainties as they propagate through all of the preceding problems, from data to model inference to prediction and finally to optimal design and control. Optimal mathematical methods and algorithms for such end-to-end, data-to-decisions modeling and simulation of complex problems—requiring integrated solution of forward, inverse, optimization, and UQ problems for large, multiphysics, multiscale models—entails challenges of the highest order, while also presenting great opportunities for applied mathematics research. Unfortunately, research on forward, inverse, optimization, and UQ problems has all-too-often progressed in isolation. This has led to mathematical methods that perform well when considering the forward simulation in isolation, but are prohibitive or suboptimal or unstable when combined with other methods within the framework of inversion, optimization, or UQ. The reverse is also true: general-purpose methods developed within the inversion, optimization, and UQ fields often become prohibitive when applied to complex CS&E problems since they do not exploit their structure.

58 GEOSCIENCES↗
DOE OSTI2015

DiaMonD: An Integrated Multifaceted Approach to Mathematics at the Interfaces of Data, Models, and Decisions

For many problems in computational science and engineering, the so-called forward problem-solution of the underlying mathematical model to yield output quantities of interest, given input parameters--is difficult enough for frontier complex models, which are often characterized by coupled multiphysics and possibly stochastic behavior over a wide range of length and time scales. However, we face the need to move beyond the forward problem, to address what is often the ultimate goal of CS&E: decision-making. This requires us to tackle a spectrum of mathematical problems that subsume, and are thus even more difficult than, the forward problem. First, given experimental data, we wish to estimate unknown parameters characterizing a model of a natural or engineered system by solving an inverse problem. Second, we seek the optimal con figuration of the system (or experiments) by solving an optimal design problem. Third, the optimal operation of the system must be determined by solving an optimal control problem. And fourth, we must quantify uncertainties as they propagate through all of the preceding problems, from data to model inference to prediction and finally to optimal design and control. Optimal mathematical methods and algorithms for such end-to-end, data-to-decisions modeling and simulation of complex problems--requiring integrated solution of forward, inverse, optimization, and UQ problems for large, multiphysics, multiscale models--entails challenges of the highest order, while also presenting great opportunities for applied mathematics research. Unfortunately, research on forward, inverse, optimization, and UQ problems has all-too-often progressed in isolation. This has led to mathematical methods that perform well when considering the forward simulation in isolation, but are prohibitive or suboptimal or unstable when combined with other methods within the framework of inversion, optimization, or UQ. The reverse is also true: general-purpose methods developed within the inversion, optimization, and UQ fields often become prohibitive when applied to complex CS&E problems since they do not exploit their structure. DiaMonD is a multi-institutional DOE MMICC effort that aims to address the challenges of end-to-end, data-to-decisions modeling and simulation for complex CS&E problems in a unifi ed, systematic, and integrated fashion. Institutions involved are Colorado State, Florida State, Los Alamos, MIT, Oak Ridge, Stanford, and UT-Austin. The goals of DiaMonD are (1) to develop advanced mathematical methods and analysis for multimodel, multiphysics, multiscale model problems driven by frontier DOE applications, including those in subsurface energy and environmental flows, materials for energy storage and conversion, and ice sheet dynamics; (2) to create theory and algorithms for integrated inversion, optimization, and uncertainty quantification for these complex problems; and (3) to disseminate the philosophy of an integrated end-to-end, data-to-decisions approach to modeling and simulation of complex problems to the broader applied math and computational science communities through workshops and other outreach.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Preliminary Comparison of Neutron Detector Sensitivities [Poster]

Nuclear data (ND) is vital to predictive simulations such as those completed with MCNP®. ND sensitivities are used to optimize the design of benchmark experiments. Diverse benchmarks (including neutron noise) are required to further our understanding of ND. Measured and simulated data of a 4.5-kg Pu sphere was used to calculate neutron noise parameters and associated ND Sensitivities

Nuclear Criticality Safety Program (NCSP)↗
DOE OSTI2019

Small-Angle Scattering as a Structural Probe for Nucleic Acid Nanoparticles (NANPs) in a Dynamic Solution Environment

Nucleic acid-based technologies are an emerging research focus area for pharmacological and biological studies because they are biocompatible and can be designed to produce a variety of scaffolds at the nanometer scale. The use of nucleic acids (ribonucleic acid (RNA) and/or deoxyribonucleic acid (DNA)) as building materials in programming the assemblies and their further functionalization has recently established a new exciting field of RNA and DNA nanotechnology, which have both already produced a variety of different functional nanostructures and nanodevices. It is evident that the resultant architectures require detailed structural and functional characterization and that a variety of technical approaches must be employed to promote the development of the emerging fields. Small-angle X-ray and neutron scattering (SAS) are structural characterization techniques that are well placed to determine the conformation of nucleic acid nanoparticles (NANPs) under varying solution conditions, thus allowing for the optimization of their design. SAS experiments provide information on the overall shapes and particle dimensions of macromolecules and are ideal for following conformational changes of the molecular ensemble as it behaves in solution. In addition, the inherent differences in the neutron scattering of nucleic acids, lipids, and proteins, as well as the different neutron scattering properties of the isotopes of hydrogen, combined with the ability to uniformly label biological macromolecules with deuterium, allow one to characterize the conformations and relative dispositions of the individual components within an assembly of biomolecules. This article will review the application of SAS methods and provide a summary of their successful utilization in the emerging field of NANP technology to date, as well as share our vision on its use in complementing a broad suite of structural characterization tools with some simulated results that have never been shared before.

Materials Science↗
NASA NTRS1983

The definition of the process of electrochemical impregnation of nickel electrodes

Electrochemical impregnation was studied during a series of experiments designed to define the optimal conditions for the fabrication of dimensionally stable cell anodes of Ni-H2 and Ni-Cd systems. The influence of various parameters, such as current and duration of electrolysis, temperature and acidity of the chemical bath, the concentrations of Ni and Co as well as the use of ethanol was determined. Results show that the electrochemical impregnation process as defined is industrially feasible and it is suggested that Ni-H2 and Ni-Cd type electrodes be produced in sufficient quantity to further evaluate their performance characteristics.

Antoine, P.↗
NASA NTRSDate not supplied

Aero-Propulsive Modeling for eVTOL Aircraft Using Wind Tunnel Testing with Multisine Inputs

A novel approach for modeling the aero-propulsive characteristics of an electric vertical takeoff and landing (eVTOL) aircraft was developed and demonstrated in wind tunnel testing. The approach was applied to the NASA LA-8 tandem tilt-wing eVTOL aircraft, using an efficient, hybrid experiment design composed of a static I-optimal response surface design for slowly-varying test variables, and dynamic orthogonal phase-optimized multisine excitations for the control surfaces and electric propulsors. Both the static and dynamic experiment designs were executed simultaneously to collect informative data for model identification. Statistically-weighted multivariate orthogonal function modeling was used to combine local modeling results computed in the frequency domain using data collected with dynamic excitations operating on the control effectors to form an aggregate aero-propulsive model. The final identified model exhibited good predictive capability when compared to validation data acquired separately from the data used to develop the model. The required test time using these new techniques was reduced by at least a factor of five compared to previous static wind tunnel testing for the LA-8 aircraft, while providing more informative data, greater parameterization flexibility, and high-quality models.

system identification↗
DOE OSTI2025

Understanding the structural mechanics of ligated DNA crystals via molecular dynamics simulation

DNA self-assembly is a highly programmable method to construct arbitrary architectures based on sequence complementarity. Among various constructs, DNA crystals are macroscopic crystalline materials formed by assembling motifs via sticky end association. Due to their high structural integrity and size ranging from tens to hundreds of micrometers, DNA crystals offer unique opportunities to study the structural properties and deformation behaviors of DNA assemblies. For example, enzymatic ligation of sticky ends can selectively seal nicks resulting in more robust structures with enhanced mechanical properties. However, the research efforts have been mostly on experiments involving different motif designs, structural optimization, or new synthesis methods, while their mechanics are not yet fully understood. The complex properties of DNA crystals are difficult to study via experiments alone, and numerical simulation can complement and aid the experiments. The coarse-grained molecular dynamics (MD) simulation is a powerful tool that can probe the mechanics of DNA assemblies. Here, we investigate DNA crystals made of four different motif lengths with various ligation patterns (full ligation, major directions, connectors, and in-plane) using oxDNA, an open-source, coarse-grained MD platform. We found that several distinct deformation stages emerge in response to mechanical loading and that the number and the location of ligated nucleotides can significantly modulate structural behaviors. These findings should be useful for predicting crystal properties and thus improving the design.

DNA crystal↗
NASA NTRS2002

Macromolecular Crystal Growth by Means of Microfluidics

We have performed a feasibility study in which we show that chip-based, microfluidic (LabChip(TM)) technology is suitable for protein crystal growth. This technology allows for accurate and reliable dispensing and mixing of very small volumes while minimizing bubble formation in the crystallization mixture. The amount of (protein) solution remaining after completion of an experiment is minimal, which makes this technique efficient and attractive for use with proteins, which are difficult or expensive to obtain. The nature of LabChip(TM) technology renders it highly amenable to automation. Protein crystals obtained in our initial feasibility studies were of excellent quality as determined by X-ray diffraction. Subsequent to the feasibility study, we designed and produced the first LabChip(TM) device specifically for protein crystallization in batch mode. It can reliably dispense and mix from a range of solution constituents into two independent growth wells. We are currently testing this design to prove its efficacy for protein crystallization optimization experiments. In the near future we will expand our design to incorporate up to 10 growth wells per LabChip(TM) device. Upon completion, additional crystallization techniques such as vapor diffusion and liquid-liquid diffusion will be accommodated. Macromolecular crystallization using microfluidic technology is envisioned as a fully automated system, which will use the 'tele-science' concept of remote operation and will be developed into a research facility for the International Space Station as well as on the ground.

vanderWoerd, Mark↗
DOE OSTI2026

Optimizing enzymes for plastic upcycling using machine learning design and high throughput experiments

Plastic use is ubiquitous in the modern world, and polyethylene terephthalate (PET) is one of the most abundantly produced plastics (and the most highly produced polyester), with ~65 million metric tons manufactured annually. To the consumer, PET is likely most recognizable as the plastic used to make beverage bottles. Like many plastics, traditional mechanical or chemical means of PET deconstruction and upcycling are costly and inefficient. Because of these challenges, recycled plastic is generally of lower quality and is more expensive to produce than virgin plastic derived from petroleum. Ultimately, this results in most plastic ending up as waste. We view plastic waste as an underutilized resource which, with the development of more efficient and high-quality recycling processes, could (1) generate significant economic value while (2) decreasing petroleum usage and greenhouse gas emissions, as well as (3) minimizing its negative environmental and health impacts. Biocatalytic recycling, or biomanufacturing the basic building blocks of new plastic from plastic waste, is a promising approach to plastic reuse that complements existing recycling technologies. Recently, biological enzymes capable of breaking down PET have garnered significant attention as an attractive means of dealing with the plastic problem. These enzymes are currently undergoing pilot studies for implementation in industrial-scale enzyme-based recycling. However, there are significant limitations to current enzymes, including the need to perform costly pre-processing of the plastic waste before the enzymes are able to work. Further optimization of these enzymes is necessary to make these technologies competitive, and ultimately incentivise industry-wide adoption of this biology-based green recycling technology. n this work we demonstrate a means to design and generate performant biological enzymes, capable of efficiently deconstructing plastic waste. Specifically, we applied recent advances in artificial intelligence, machine learning, and statistical analysis to design new versions and discover natural enzymes capable of breaking down PET. We focused on optimizing key properties that are important for industrial-scale enzymatic recycling such as pH and thermotolerance. Normal testing of enzymatic plastic-deconstruction is extremely labor intensive and so through this work we also developed a robotic-assisted experimental pipeline capable of characterizing thousands of candidate enzymes. The results of this iterative, AI-guided, multi-discipline approach have led to increases in enzymatic breakdown of over 150X over starting enzymes. This work supports the rapidly developing and transformative field of biocatalytic solutions to environmental problems beyond the discovery and predictive understanding of enzymes for polymer recycling, and has wide implications for tackling numerous energy problems such as carbon capture and fixation (e.g., engineering carbon monoxide dehydrogenase and the rubisco-pathway), biomining (e.g., design of lanthanide-binding proteins) and biomanufacturing (e.g., lignin-deconstruction enzymes).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Multidisciplinary Design, Analysis, and Optimization (MDO) for Co-Designed Transmission & Distribution Electric Grid Planning

This paper describes early experiences and example use cases applying multi-disciplinary design analysis and optimization (MDO) to the integrated design of power grids. Adapted from aerospace, MDO enables combining multiple existing tools into a coordinated optimization. Here we use MDO to simultaneously capture integrated transmission-distribution and investment-engineering trade-offs in an automated framework. Example use cases showcase prototype interactions among existing grid models using MDO and hint at the types of integrated analyses enabled by this approach. In addition, we share experiences and thoughts on grid-specific challenges and opportunities to help advance further work in this area.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

Comparing D-optimal designs with common mixture experimental designs for logistic regression

Mixture experiments are used in applications where the proportion of mixing components affects a response variable, such as in studies involving chemical formulations. In many applications, the response is dichotomous or binary (e.g., pass or fail) and a concern for researchers is how to efficiently and informatively design such experiments. A naive approach is to use design recommendations derived from linear normal-theory models with constant variance. In this research, we investigate the potential risks of such designs by comparing them to D-optimal mixture designs for binary responses and evaluating the D-efficiency of these design alternatives for several parameter subspaces. Standard designs for normal theory models generally do not work well for binary responses due to the tendency of these designs to favor boundary points. In addition, D-optimal mixture designs for binary responses tend to locate design points in the region where the magnitude of predicted response probabilities are moderate. Lastly, we recommend that researchers pay close attention to what is known about the characteristics of the underlying process models in selecting appropriate mixture designs for binary-response applications.

D-optimality↗
DOE OSTI2025

Optimization of the BDX experiment for Light Dark Matter searches at JLab

The Light Dark Matter (LDM) hypothesis postulates the existence of a new class of sub GeV particles, neutral under Standard Model (SM) interactions. In its simplest form, LDM consists of particles ¿ with masses below 1 GeV/c2, interacting with SM particles via a new force mediated by a light, spin-1 boson A0, commonly referred to as “Dark Photon”. This framework envisions a distinct “Dark Sector” with its own particles and interactions, offering a theoretically well motivated explanation for Dark Matter, consistent with astrophysical observations and a thermal production mechanism. The Beam Dump eXperiment (BDX) is an approved experiment at Jefferson Lab designed to search for Light Dark Matter. BDX will utilize an 11 GeV electron beam impinging on a thick target to produce a forward-boosted secondary beam of Light Dark Matter particles, which will then be detected by a dedicated downstream detector. Approved in 2018, BDX is expected to be commissioned in 2026 and run in 2027-2029. My thesis focuses on the preparatory work to deploy the BDX experiment. The detector design has been optimized to balance practicality with enhanced Light Dark Matter detection capabilities. Extensive characterization of detector components has been performed to ensure a precise understanding of detector response. A custom Monte Carlo framework has been developed to simulate Light Dark Matter signal and explore various theoretical models of interest. Additionally, a comprehensive data analysis framework has been developed to maximize the experiment sensitivity to Light Dark Matter, with optimizations based on expected detector performance. The ultimate goal of this thesis is to optimize BDX as a flagship experiment in Light Dark Matter searches, enabling it to probe different Dark Matter models.

Spreafico, M. [Univ. of Genova (Italy)]↗
NASA NTRS2003

Aging Optimization of Aluminum-Lithium Alloy C458 for Application to Cryotank Structures

This viewgraph report presents an examination of the fracture toughness of aluminum-lithium alloy C458 for use in cryotank structures. Topics cover include: cryogenics, alloy composition, strengthing precipitates in C458, cryogenic fracture toughness improvements, design of experiments for measuring aging optimization of C458 plate and effects of aging of properties of C458 plate.

Sova, B. J.↗
DOE OSTI2020

Closed-loop optimization of fast-charging protocols for batteries with machine learning

Simultaneously optimizing many design parameters in time-consuming experiments causes bottlenecks in a broad range of scientific and engineering disciplines. One such example is process and control optimization for lithium-ion batteries during materials selection, cell manufacturing and operation. A typical objective is to maximize battery lifetime; however, conducting even a single experiment to evaluate lifetime can take months to years. Furthermore, both large parameter spaces and high sampling variability necessitate a large number of experiments. As such, the key challenge is to reduce both the number and the duration of the experiments required. Here we develop and demonstrate a machine learning methodology to efficiently optimize a parameter space specifying the current and voltage profiles of six-step, ten-minute fast-charging protocols for maximizing battery cycle life, which can alleviate range anxiety for electric-vehicle users. We combine two key elements to reduce the optimization cost: an early-prediction model, which reduces the time per experiment by predicting the final cycle life using data from the first few cycles, and a Bayesian optimization algorithm, which reduces the number of experiments by balancing exploration and exploitation to efficiently probe the parameter space of charging protocols. Using this methodology, we rapidly identify high-cycle-life charging protocols among 224 candidates in 16 days (compared with over 500 days using exhaustive search without early prediction), and subsequently validate the accuracy and efficiency of our optimization approach. Our closed-loop methodology automatically incorporates feedback from past experiments to inform future decisions and can be generalized to other applications in battery design and, more broadly, other scientific domains that involve time-intensive experiments and multi-dimensional design spaces.

25 ENERGY STORAGE↗