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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 1,459 records · Page 81
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

A POD reduced order model for resolving angular direction in neutron/photon transport problems

This article presents the first Reduced Order Model (ROM) that efficiently resolves the angular dimension of the time independent, mono-energetic Boltzmann Transport Equation (BTE). It is based on Proper Orthogonal Decomposition (POD) and uses the method of snapshots to form optimal basis functions for resolving the direction of particle travel in neutron/photon transport problems. A unique element of this work is that the snapshots are formed from the vector of angular coefficients relating to a high resolution expansion of the BTE's angular dimension. In addition, the individual snapshots are not recorded through time, as in standard POD, but instead they are recorded through space. In essence this work swaps the roles of the dimensions space and time in standard POD methods, with angle and space respectively. It is shown here how the POD model can be formed from the POD basis functions in a highly efficient manner. The model is then applied to two radiation problems; one involving the transport of radiation through a shield and the other through an infinite array of pins. Both problems are selected for their complex angular flux solutions in order to provide an appropriate demonstration of the model's capabilities. It is shown that the POD model can resolve these fluxes efficiently and accurately. In comparison to high resolution models this POD model can reduce the size of a problem by up to two orders of magnitude without compromising accuracy. Solving times are also reduced by similar factors.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Atlas of putative minima and low-lying energy networks of water clusters n = 3 - 25

We report a database consisting of the putative minima and ~3.2 million local minima lying within 5 kcal/mol from the putative minima for water clusters of sizes n = 3 – 25 using an improved version of the Monte Carlo Temperature Basin Paving (MCTBP) global optimization procedure in conjunction with the ab-initio based, flexible, polarizable Thole-Type Model (TTM2.1-F, version 2.1) interaction potential for water. Several of the low-lying structures, as well as lowlying penta-coordinated water networks obtained with the TTM2.1-F potential, were further refined at the Møller-Plesset second order perturbation MP2/aug-cc-pVTZ level of theory. In total, we have identified 3,138,303 networks corresponding to local minima of the clusters n = 3 – 25, whose Cartesian coordinates and relative energies can be obtained from the webpage https://sites.uw.edu/wdbase/. Networks containing penta-coordinated water molecules start to appear at n = 11 and, quite surprisingly, are energetically close (within 1 – 3 kcal/mol) to the putative minima, a fact that has been confirmed from the MP2 calculations. This large database of water cluster minima spanning quite dissimilar hydrogen bonding networks is expected to influence the development and assessment of the accuracy of interaction potentials for water as well as lower scaling electronic structure methods (such as different density functionals). Furthermore, it can also be used in conjunction with data science approaches (including but no limited to neural networks, machine and deep learning) to understand the properties of water, nature’s most important substance.

Rakshit, Avijit↗
DOE OSTI2025

A case study in contrastive learning information combination: Application to technical forensics of additive manufacturing filament source identification

Combination of information from disparate data sources into a single decision is a core challenge in many fields, including the field of technical forensics. Technical forensics (TF) utilizes technical characterization of questioned samples to determine properties of that sample; these properties are then used to infer information of forensic interest, such as provenance, age, or attribution. TF is utilized in traditional forensic applications, such as the attribution of material fragments from an explosive, and in nuclear forensic applications, such as the attribution of actinides which have been interdicted out of regulatory control. The challenge of combining information from disparate sources, described alternately by many terms including “Data Fusion” and “Data Integration”, is exacerbated in the technical forensics domain due to at least two factors: the challenge of interpreting each information source singularly, and the relatively small data set sizes available. Extensive literature exists attempting to combine technical forensics information sources, both in manual and automated processes. These attempts are often bespoke to the specific information sources (such as the bi-, tri-, or quad-isotope chart (Moody, Grant, and Hutcheon 2005)), with some emerging examples of simple early- and late- fusion (, respectively). Simultaneous to the information combination efforts described in the previous paragraph, the field of natural language processing attempted (and largely succeeded) in combining information from multiple non-technical information sources. The ecosystem of “multi-modal” language models, which can take text and images as input, and generate text and images as output, became large and diverse by 2025 (Khan et al. 2025). In a generalized sense, many of these methods are trained by learning neural networks which can convert raw text or images into a vector of numbers describing the text or image, hereafter called “embeddings” and the neural networks performing the conversion are called “embedders”. By using a separate embedder for text and images, finding coincident text and images (such as images with their captions), and optimizing the parameters of the embedders such that the embeddings for the text and the image are similar, the field has found a bridge between text and images (Girdhar et al. 2023). It is the contention of the authors of this report that this insight is not limited to text and images but instead can be extended to any modality which can be found coincidently. The subject of the rest of this report is the application of this method to example multi-modal technical forensic data. Some details about the data used in this report are not appropriate for this report, and are included in a companion report (PNNL-38669).

36 MATERIALS SCIENCE↗
DOE OSTI2017

Photocatalytic activity of ZnO nanoparticles with optimization of defects

Graphical abstract: Systematic scheme for photocatalytic activity. Pristine and air quenched ZnO nanoparticles were effectively synthesized by simple combustion method and effectively used as photocatalyst for photodegradtion of methyl orange dye. It was observed that air quenched ZnO nanoparticles shows the better properties as compared to pristine ZnO. The complete degradation of dye was observed in 150 min. - Highlights: • Pristine and air quenched ZnO nanoparticles were synthesized by simple combustion method. • Synthesized ZnO for air quenched ZnO has better optoelectronic properties. • ZnO nanoparticles for air quenched ZnO exhibiting complete photodegradation of Methyl Orange dye under UV irradiation. • Photocatalytic activities for all the samples were observed in 150 min. - Abstract: Tuning of defect levels is the major concern for the application of ZnO nanoparticles. These nanoparticles with different defect concentration were prepared by simple combustion method at 700 °C and subsequent quenching in air. The catalyst was characterized by X-Ray Diffraction (XRD), Field Emission Scanning Electron Microscope (FESEM), UV–vis spectroscopy measurements. XRD reveals that the cell volume increased with large temperature difference between 700 °C and quenched media. FTIR confirmed that the vibrational frequencies shifted from 519 cm{sup −1} to 535 cm{sup −1} point out the role of quenching defects. FESEM shows the size is of the order of 20–45 nm and found irregular distribution of grain boundaries in the quenched induced defects. Different morphology plays a major role in the enhancement of photocatalytic efficiency of synthesized nanoparticles. PL intensity indicates more oxygen vacancies for air quenched ZnO whereas; DTA study demonstrates that the synthesized ZnO is thermally stable. Kinetics study confirmed the photodegradation of methyl orange (MO) dye using this photocatalyst follows first order rate constant. It exhibits the complete degradation of dye only in 150 min under UV irradiation. Kinetic study revealed the rate constant of nanoparticles is 0.0165 min{sup −1}. Air quenched sample showed the higher intensity of defect band as compared to pristine ZnO.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Ultra‐Flexible Pixelated Perovskite Photodetectors Enabled by Honeycomb Polymer Grids for High‐Resolution Imaging

Abstract A nature‐inspired fabrication method based on a photolithography‐free flexible polymer grid is reported for high‐resolution pixelation of perovskite photodiode arrays with exceptional mechanical ductility and a morphology resembling that of natural compound eyes. The resulting pixelated perovskite photosensitive layer has a ≈1 µm pixel size with 2000 Pixels per inch (PPI) resolution when fully assembled as a photodetector array, delivering a detectivity of >10 13 Jones while providing cross‐talk free imaging. Using a polymer grid effectively releases stress on the perovskite platform, greatly increasing the mechanical agility of the otherwise brittle perovskite film. This novel fabrication methodology and device design offer new possibilities for applications in robotics, biomedical imaging, and virtual and augmented reality.

Zheng, Ding [Department of Chemistry and the Mater↗
DOE OSTI2024

Nth-plant scenario for blended pellets of Miscanthus, Switchgrass, and Corn Stover using multi-modal transportation: Biorefineries and depots in the contiguous U.S.

The sustainability of the biofuel industry depends on the development of a mature conversion technology on a national level that can take advantage of the economies of scale: the nth-plant. Here, this study addresses the logistic challenge of mobilizing national cellulosic feedstock supplies for a sustainable bioenergy industry. A Mixed Integer Linear Programming (MILP) model was developed and updated to deliver on-spec biomass that considers both a desired quantity and quality at the biorefinery. Our supply chain analysis includes multi-modal transport (truck and rail), varying depot and biorefinery sizes, and feedstock blends of corn stover (harvested by either a two- or three-pass method), switchgrass, and miscanthus. The following US states: Illinois, Kansas, Missouri, North Carolina, Oklahoma, Georgia, and Texas were identified as key locations for producing accessible miscanthus. Based on our most optimistic scenario, using trucks as the only transportation mode in 2040 with a cost target of $\$$79/dt, corn stover, switchgrass, and miscanthus could help meet 48% of the EPA target, 173 million dry tons that translate into 7.8 billion GGE. The addition of rail transportation for biomass delivery to biorefineries could help meet 79% of the EPA target, 283 million dry tons that translate into 12.7 billion GGE.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2018

A detailed examination of laser-ion acceleration mechanisms in the relativistic transparency regime using tracers

Here we present a particle-in-cell study of linearly polarized laser-ion acceleration systems, in which we use both two-dimensional (2D) and three-dimensional (3D) simulations to characterize the ion acceleration mechanisms in targets which become transparent to the laser pulse during irradiation. First, we perform a target length scan to optimize the peak ion energies in both 2D and 3D, and the predictive capabilities of 2D simulations are discussed. Tracer analysis allows us to isolate the acceleration into stages of target normal sheath acceleration (TNSA), hole boring (HB), and break-out afterburner (BOA) acceleration, which vary in effectiveness based on the simulation parameters. The thinnest targets reveal that enhanced TNSA is responsible for accelerating the most energetic ions, whereas the thickest targets have ions undergoing successive phases of HB and TNSA (in 2D) or BOA and TNSA (in 3D); HB is not observed to be a dominant acceleration mechanism in the 3D simulations. It is in the intermediate optimal regime, both when the laser breaks through the target with appreciable amplitude and when there is enough plasma to form a sustained high density flow, that BOA is most effective and is responsible for the most energetic ions. Eliminating the transverse laser spot size effects by performing a plane wave simulation, we can isolate with greater confidence the underlying physics behind the ion dynamics we observe. Specifically, supplemented by wavelet and FFT analyses, we match the post-transparency BOA acceleration with a wave-particle resonance with a high-amplitude low-frequency electrostatic wave of increasing phase velocity, consistent with that predicted by the Buneman instability.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2014

TH-C-BRD-05: Reducing Proton Beam Range Uncertainty with Patient-Specific CT HU to RSP Calibrations Based On Single-Detector Proton Radiography

Purpose: Beam range uncertainty in proton treatment comes primarily from converting the patient's X-ray CT (xCT) dataset to relative stopping power (RSP). Current practices use a single curve for this conversion, produced by a stoichiometric calibration based on tissue composition data for average, healthy, adult humans, but not for the individual in question. Proton radiographs produce water-equivalent path length (WEPL) maps, dependent on the RSP of tissues within the specific patient. This work investigates the use of such WEPL maps to optimize patient-specific calibration curves for reducing beam range uncertainty. Methods: The optimization procedure works on the principle of minimizing the difference between the known WEPL map, obtained from a proton radiograph, and a digitally-reconstructed WEPL map (DRWM) through an RSP dataset, by altering the calibration curve that is used to convert the xCT into an RSP dataset. DRWMs were produced with Plastimatch, an in-house developed software, and an optimization procedure was implemented in Matlab. Tests were made on a range of systems including simulated datasets with computed WEPL maps and phantoms (anthropomorphic and real biological tissue) with WEPL maps measured by single detector proton radiography. Results: For the simulated datasets, the optimizer showed excellent results. It was able to either completely eradicate or significantly reduce the root-mean-square-error (RMSE) in the WEPL for the homogeneous phantoms (to zero for individual materials or from 1.5% to 0.2% for the simultaneous optimization of multiple materials). For the heterogeneous phantom the RMSE was reduced from 1.9% to 0.3%. Conclusion: An optimization procedure has been designed to produce patient-specific calibration curves. Test results on a range of systems with different complexities and sizes have been promising for accurate beam range control in patients. This project was funded equally by the Engineering and Physical Sciences Research Council (UK) and Ion Beam Applications (Louvain-La-Neuve, Belgium)

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2022

An automated approach to the alignment of compound refractive lenses

Compound refractive lenses (CRLs) are established X-ray focusing optics, and are used to focus the beam or image the sample in many beamlines at X-ray facilities. While CRLs are quite established, the stack of single lens elements affords a very small numerical aperture because of the thick lens profile, making them far more difficult to align than classical optical lenses that obey the thin-lens approximation. This means that the alignment must be very precise and is highly sensitive to changes to the incident beam, often requiring regular readjustments. Some groups circumvent the full realignment procedure by using engineering controls ( e.g. mounting optics) that sacrifice some of the beam's focusing precision, i.e. spot size, or resolution. While these choices minimize setup time, there are clear disadvantages. This work presents a new automated approach to align CRLs using a simple alignment apparatus that is easy to adapt and install at different types of X-ray experiments or facilities. This approach builds on recent CRL modeling efforts, using an approach based on the Stochastic Nelder–Mead (SNM) simplex method. This method is outlined and its efficacy is demonstrated with numerical simulation that is tested in real experiments conducted at the Advanced Photon Source to confirm its performance with a synchrotron beam. This work provides an opportunity to automate key instrumentation at X-ray facilities.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2013

Multivariate Normal Tissue Complication Probability Modeling of Heart Valve Dysfunction in Hodgkin Lymphoma Survivors

Purpose: To establish a multivariate normal tissue complication probability (NTCP) model for radiation-induced asymptomatic heart valvular defects (RVD). Methods and Materials: Fifty-six patients treated with sequential chemoradiation therapy for Hodgkin lymphoma (HL) were retrospectively reviewed for RVD events. Clinical information along with whole heart, cardiac chambers, and lung dose distribution parameters was collected, and the correlations to RVD were analyzed by means of Spearman's rank correlation coefficient (Rs). For the selection of the model order and parameters for NTCP modeling, a multivariate logistic regression method using resampling techniques (bootstrapping) was applied. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC). Results: When we analyzed the whole heart, a 3-variable NTCP model including the maximum dose, whole heart volume, and lung volume was shown to be the optimal predictive model for RVD (Rs = 0.573, P<.001, AUC = 0.83). When we analyzed the cardiac chambers individually, for the left atrium and for the left ventricle, an NTCP model based on 3 variables including the percentage volume exceeding 30 Gy (V30), cardiac chamber volume, and lung volume was selected as the most predictive model (Rs = 0.539, P<.001, AUC = 0.83; and Rs = 0.557, P<.001, AUC = 0.82, respectively). The NTCP values increase as heart maximum dose or cardiac chambers V30 increase. They also increase with larger volumes of the heart or cardiac chambers and decrease when lung volume is larger. Conclusions: We propose logistic NTCP models for RVD considering not only heart irradiation dose but also the combined effects of lung and heart volumes. Our study establishes the statistical evidence of the indirect effect of lung size on radio-induced heart toxicity.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2015

Riverbed Clogging Associated with a California Riverbank Filtration System: An Assessment of Mechanisms and Monitoring Approaches

An experimental field study was performed to investigate riverbed clogging processes and associated monitoring approaches near a dam-controlled riverbank filtration facility in Northern California. Motivated by previous studies at the site that indicated riverbed clogging plays an important role in the performance of the riverbank filtration system, we investigated the spatiotemporal variability and nature of the clogging. In particular, we investigated whether the clogging was due to abiotic or biotic mechanisms. A secondary aspect of the study was the testing of different methods to monitor riverbed clogging and related processes, such as seepage. Monitoring was conducted using both point-based approaches and spatially extensive geophysical approaches, including: grain-size analysis, temperature sensing, electrical resistivity tomography, seepage meters, microbial analysis, and cryocoring, along two transects. The point monitoring measurements suggested a substantial increase in riverbed biomass (2 orders of magnitude) after the dam was raised compared to the small increase (~2%) in fine-grained sediment. These changes were concomitant with decreased seepage. The decreased seepage eventually led to the development of an unsaturated zone beneath the riverbed, which further decreased infiltration capacity. Comparison of our time-lapse grain-size and biomass datasets suggested that biotic processes played a greater role in clogging than did abiotic processes. Cryocoring and autonomous temperature loggers were most useful for locally monitoring clogging agents, while electrical resistivity data were useful for interpreting the spatial extent of a pumping-induced unsaturated zone that developed beneath the riverbed after riverbed clogging was initiated. The improved understanding of spatiotemporally variable riverbed clogging and monitoring approaches is expected to be useful for optimizing the riverbank filtration system operations.

58 GEOSCIENCES↗
DOE OSTI2026

F-Hash: Feature-Based Hash Design for Time-Varying Volume Visualization via Multi-Resolution Tesseract Encoding

Interactive time-varying volume visualization is challenging due to its complex spatiotemporal features and sheer size of the dataset. Recent works transform the original discrete time-varying volumetric data into continuous Implicit Neural Representations (INR) to address the issues of compression, rendering, and super-resolution in both spatial and temporal domains. However, training the INR takes a long time to converge, especially when handling large-scale time-varying volumetric datasets. In this work, we proposed F-Hash, a novel feature-based multi-resolution Tesseract encoding architecture to greatly enhance the convergence speed compared with existing input encoding methods for modeling time-varying volumetric data. The proposed design incorporates multi-level collision-free hash functions that map dynamic 4D multi-resolution embedding grids without bucket waste, achieving high encoding capacity with compact encoding parameters. Our encoding method is agnostic to time-varying feature detection methods, making it a unified encoding solution for feature tracking and evolution visualization. Experiments show the F-Hash achieves state-of-the-art convergence speed in training various time-varying volumetric datasets for diverse features. We also proposed an adaptive ray marching algorithm to optimize the sample streaming for faster rendering of the time-varying neural representation.

deep learning↗
DOE OSTI2015

Nonparametric variational optimization of reaction coordinates

State of the art realistic simulations of complex atomic processes commonly produce trajectories of large size, making the development of automated analysis tools very important. A popular approach aimed at extracting dynamical information consists of projecting these trajectories into optimally selected reaction coordinates or collective variables. For equilibrium dynamics between any two boundary states, the committor function also known as the folding probability in protein folding studies is often considered as the optimal coordinate. To determine it, one selects a functional form with many parameters and trains it on the trajectories using various criteria. A major problem with such an approach is that a poor initial choice of the functional form may lead to sub-optimal results. Here, we describe an approach which allows one to optimize the reaction coordinate without selecting its functional form and thus avoiding this source of error.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

Universal tight binding model for chemical reactions in solution and at surfaces. I. Organic molecules

As is now well established, a first order expansion of the Hohenberg–Kohn total energy density functional about a trial input density, namely, the Harris–Foulkes functional, can be used to rationalize a non self consistent tight binding model. If the expansion is taken to second order then the energy and electron density matrix need to be calculated self consistently and from this functional one can derive a charge self consistent tight binding theory. In this paper we have used this to describe a polarizable ion tight binding model which has the benefit of treating charge transfer in point multipoles. This admits a ready description of ionic polarizability and crystal field splitting. It is necessary in constructing such a model to find a number of parameters that mimic their more exact counterparts in the density functional theory. We describe in detail how this is done using a combination of intuition, exact analytical fitting, and a genetic optimization algorithm. Having obtained model parameters we show that this constitutes a transferable scheme that can be applied rather universally to small and medium sized organic molecules. We have shown that the model gives a good account of static structural and dynamic vibrational properties of a library of molecules, and finally we demonstrate the model's capability by showing a real time simulation of an enolization reaction in aqueous solution. In two subsequent papers, we show that the model is a great deal more general in that it will describe solvents and solid substrates and that therefore we have created a self consistent quantum mechanical scheme that may be applied to simulations in heterogeneous catalysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Optimization and Multimachine Learning Algorithms to Predict Nanometal Surface Area Transfer Parameters for Gold and Silver Nanoparticles

Interactions between gold metallic nanoparticles and molecular dyes have been well described by the nanometal surface energy transfer (NSET) mechanism. However, the expansion and testing of this model for nanoparticles of different metal composition is needed to develop a greater variety of nanosensors for medical and commercial applications. In this study, the NSET formula was slightly modified in the size-dependent dampening constant and skin depth terms to allow for modeling of different metals as well as testing the quenching effects created by variously sized gold, silver, copper, and platinum nanoparticles. Overall, the metal nanoparticles followed more closely the NSET prediction than for Förster resonance energy transfer, though scattering effects began to occur at 20 nm in the nanoparticle diameter. To further improve the NSET theoretical equation, an attempt was made to set a best-fit line of the NSET theoretical equation curve onto the Au and Ag data points. An exhaustive grid search optimizer was applied in the ranges for two variables, 0.1≤C≤2.0 and 0≤α≤4, representing the metal dampening constant and the orientation of donor to the metal surface, respectively. Three different grid searches, starting from coarse (entire range) to finer (narrower range), resulted in more than one million total calculations with values C=2.0 and α=0.0736. The results improved the calculation, but further analysis needed to be conducted in order to find any additional missing physics. With that motivation, two artificial intelligence/machine learning (AI/ML) algorithms, multilayer perception and least absolute shrinkage and selection operator regression, gave a correlation coefficient, R2, greater than 0.97, indicating that the small dataset was not overfitting and was method-independent. This analysis indicates that an investigation is warranted to focus on deeper physics informed machine learning for the NSET equations.

Demers, Steven M. E. (ORCID:0000000192213246)↗
DOE OSTI2017

Establishing Evidence-Based Indications for Proton Therapy: An Overview of Current Clinical Trials

Purpose: To review and assess ongoing proton beam therapy (PBT) clinical trials and to identify major gaps. Methods and Materials: Active PBT clinical trials were identified from (clinicaltrials.gov) and the World Health Organization International Clinical Trials Platform Registry. Data on clinical trial disease site, age group, projected patient enrollment, expected start and end dates, study type, and funding source were extracted. Results: A total of 122 active PBT clinical trials were identified, with target enrollment of >42,000 patients worldwide. Ninety-six trials (79%), with a median planned sample size of 68, were classified as interventional studies. Observational studies accounted for 21% of trials but 71% (n=29,852) of planned patient enrollment. The most common PBT clinical trials focus on gastrointestinal tract tumors (21%, n=26), tumors of the central nervous system (15%, n=18), and prostate cancer (12%, n=15). Five active studies (lung, esophagus, head and neck, prostate, breast) will randomize patients between protons and photons, and 3 will randomize patients between protons and carbon ion therapy. Conclusions: The PBT clinical trial portfolio is expanding rapidly. Although the majority of ongoing studies are interventional, the majority of patients will be accrued to observational studies. Future efforts should focus on strategies to encourage optimal patient enrollment and retention, with an emphasis on randomized, controlled trials, which will require support from third-party payers. Results of ongoing PBT studies should be evaluated in terms of comparative effectiveness, as well as incremental effectiveness and value offered by PBT in comparison with conventional radiation modalities.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2023

Exploring Parameter Redundancy in the Unitary Coupled-Cluster Ansätze for Hybrid Variational Quantum Computing

One of the commonly used chemical-inspired approaches in variational quantum computing is the unitary coupled-cluster (UCC) ansatze. Despite being a systematic way of approaching the exact limit, the number of parameters in the standard UCC ansatze exhibits unfavorable scaling with respect to the system size, hindering its practical use on near-term quantum devices. Efforts have been taken to propose some variants of UCC ansatze with better scaling. In this paper we explore the parameter redundancy in the preparation of unitary coupled-cluster singles and doubles (UCCSD) ansatze employing spin-adapted formulation, small amplitude filtration, and entropy-based orbital selection approaches. Numerical results of using our approach on some small molecules have exhibited a significant cost reduction in the number of parameters to be optimized and in the time to convergence compared with conventional UCCSD-VQE simulations. Further, we also discuss the potential application of some machine learning techniques in further exploring the parameter redundancy, providing a possible direction for future studies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗