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At least 757 records · Page 42

Current algebra approach to two-dimensional interacting chiral metals

In this study, we reinterpret the chiral U(N) Wess-Zumino-Witten (WZW) model at level k>1 in (1+1) dimensions as an interacting chiral metal in two space dimensions. In this reinterpretation, spatial translations along one of the spatial dimensions in the two-dimensional chiral metal arise from a generator of the U(N) symmetry of the WZW model. The WZW model at k=1 is equivalent to Balents and Fisher's free chiral metal. Here, the U(N) symmetry corresponds to the IR symmetry of a chiral Fermi gas with (half of a) Fermi surface, with N equal to the number of points on the Fermi surface. We argue that exactly solvable interacting generalizations occur for levels k>1. Importantly, these interacting chiral metals maintain the U(N) symmetry of the free system. We calculate two-point correlation functions of the single-particle fermion operator, the U⁡(1) number density, and current operators in these theories for general k. We find that interactions (k>1) produce 1/N corrections to scaling of the single-particle fermion operator as N→∞ and renormalize the amplitudes of the density and current two-point functions. This construction illustrates the ersatz Fermi liquid proposal of Else et al.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Reconfiguring the band-edge states of photovoltaic perovskites by conjugated organic cations

The band edges of metal-halide perovskites with a general chemical structure of ABX 3 (A, usually a monovalent organic cation; B, a divalent cation; and X, a halide anion) are constructed mainly of the orbitals from B and X sites. Hence, the structural and compositional varieties of the inorganic B–X framework are primarily responsible for regulating their electronic properties, whereas A-site cations are thought to only help stabilize the lattice and not to directly contribute to near-edge states. We report a π-conjugation–induced extension of electronic states of A-site cations that affects perovskite frontier orbitals. The π-conjugated pyrene-containing A-site cations electronically contribute to the surface band edges and influence the carrier dynamics, with a properly tailored intercalation distance between layers of the inorganic framework. Lastly, the ethylammonium pyrene increased hole mobilities, improved power conversion efficiencies relative to that of a reference perovskite, and enhanced device stability.

36 MATERIALS SCIENCE↗

Hidden depths in the local Universe: The Stellar Stream Legacy Survey

Context. Mergers and tidal interactions between massive galaxies and their dwarf satellites are a fundamental prediction of the Lambda-cold dark matter cosmology. These events are thought to provide important observational diagnostics of non-linear structure formation. Stellar streams in the Milky Way and Andromeda are spectacular evidence for ongoing satellite disruption. However, constructing a statistically meaningful sample of tidal streams beyond the Local Group has proven a daunting observational challenge, and the full potential for deepening our understanding of galaxy assembly using stellar streams has yet to be realised. Aims. Here we introduce the Stellar Stream Legacy Survey, a systematic imaging survey of tidal features associated with dwarf galaxy accretion around a sample of ~3100 nearby galaxies within z~0.02, including about 940 Milky Way analogues. Methods. Our survey exploits public deep imaging data from the DESI Legacy Imaging Surveys, which reach surface brightness as faint as ~29 mag arcsec –2 in the r band. As a proof of concept of our survey, we report the detection and broad-band photometry of 24 new stellar streams in the local Universe. Results. We discuss how these observations can yield new constraints on galaxy formation theory through comparison to mock observations from cosmological galaxy simulations. These tests will probe the present-day mass assembly rate of galaxies, the stellar populations and orbits of satellites, the growth of stellar halos, and the resilience of stellar disks to satellite bombardment.

79 ASTRONOMY AND ASTROPHYSICS↗

Detonation Waves in High Explosives

A material at high temperature can react or decompose. For an energetic material, the reaction is exothermic and releases chemical energy that would further increase the temperature. Under some circumstances, when a reaction is triggered, such a reaction can propagate and the material rapidly releases a large amount of energy giving rise to an explosion. Examples of such materials are aerosols, suspensions of solid particles or liquid droplets in a gas; such as coal dust, grain dust and fuel-air explosions. Frequently, explosions are due to accidents. A spectacularly destructive example is the recent explosion of a large quantity of ammonium nitrate (thousands of tons) in Beirut, Lebanon (August 2020); see for example Beirut explosion. Ammonium nitrate is used as a fertilizer. It and the aerosols are not considered to be explosives due to the limited conditions for which an explosion can occur. An aerosol gets the oxidizer from the surrounding air. Burning requires diffusion of the oxidizer to the particle surface where the reaction occurs. A large density of small particles is required for a fast enough reaction to support an explosion. In contrast, an explosive is an energetic material with both fuel and oxidizer mixed on a molecular scale (either premixed gases or within molecules of a solid). This allows fast enough reactions over a wide range of conditions to support a self-propagating reactive wave known as a detonation wave. A detonation wave can be controlled and an explosive used for useful purposes such as in mining, construction, demolition, explosive welding, argon flash lamp, pulsed power using a magnetic flux generator [see also Goforth et al., 2015], jet cutter with shaped charge, explosive art, and generating conditions to study the response of materials at high strain rates and high pressures [see for example, Marsh, 1980]. Explosives are also used in conventional munitions and nuclear weapons. The focus of this book is on the theory and phenomenology of solid high explosives (HEs); in particular, plastic-bonded explosives (PBXs). Some aspects of detonation wave theory are needed to interpret explosive data. Hence, the theory is presented before the detonation wave phenomenology. A familiarity with fluid flow, specifically the notion of shock waves and the shock loci are assumed. In the remainder of this chapter we give a brief overview on the basic properties of detonation waves and PBXs.

36 MATERIALS SCIENCE↗

Constructing efficient CuO x -CeO 2 catalyst for NO reduction by CO: New insights into the structure–activity relationship

CuO-CeO 2 based materials have been recognized as promising substitutes for precious metal catalysts in emission control field due to their superior redox property and low cost. In this work, by optimizing the deposition process of CeO 2 and CuO onto γ-Al 2 O 3 , highly dispersed CuO clusters on unique CeO 2 -Al 2 O 3 support with small CeO 2 particles (7Cu-Ce/CeAl) were successfully constructed for efficient NO reduction by CO, which exhibited much higher NO removal efficiency and N 2 selectivity than CuO catalysts supported on γ-Al 2 O 3 (7Cu/Al) and conventional CeO 2 -Al 2 O 3 support (7Cu/CeAl). Moreover, H 2 O showed limited inhibition effect on the catalytic performance of 7Cu-Ce/CeAl catalyst. With the help of Raman spectra, X-ray absorption spectroscopy, in situ diffuse reflectance infrared Fourier transform spectroscopy, etc., it was clearly revealed that the abundant Cu + /Ce 3+ paired sites with surface synergetic oxygen vacancies (SSOV) on 7Cu-Ce/CeAl catalyst could effectively facilitate the adsorption and activation of CO and NO, thus significantly enhancing the NO removal efficiency.

36 MATERIALS SCIENCE↗

Understanding Binder–Silicon Interactions during Slurry Processing

The conformation of poly(acrylic acid) (PAA) as a function of shear during slurry processing to construct silicon-based anodes is elucidated via rheology-coupled ultra-small-angle neutron scattering (rheo-USANS). Rheo-USANS shows that PAA with encapsulated silicon exists as discrete aggregates that do not interact with one another. As a result, a well-connected matrix of silicon and carbon black dispersed in PAA does not exist; thus, the electrode is inhomogeneous. Raman mapping and X-ray photoelectron spectroscopy were used to confirm the electrode heterogeneity and further understand the cycling properties. These results are correlated to silicon surface chemistry to provide a pathway to making better electrodes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Systematic evaluation of magnetic sensitivities of anisotropic magnetoresistive sensors at liquid helium temperature for superconducting cavities

Trapped magnetic flux in bulk superconductors reduces the quality factor Q in superconducting radio-frequency (SRF) cavities. However, the mechanisms underlying flux trapping and radio-frequency loss are not well understood. Detailed observation of the magnetic distributions is important for understanding such phenomena. Magnetic field mapping is useful for observing the magnetic field distribution around SRF cavities. Measuring the change in the magnetic field around the cavity elucidates the flux trapping behavior. Anisotropic magnetoresistive (AMR) sensors are inexpensive and small devices that can detect magnetic flux density. The magnetic sensitivities of AMR sensors need to be evaluated at liquid helium temperature for the magnetic field mapping of SRF cavities. In this study, a test stand was constructed to calibrate the magnetic sensitivities of AMR sensors in liquid helium, and 110 AMR sensors were tested using this stand. The magnetic sensitivities were evaluated systematically. A solenoid coil was used to control the uniform external magnetic field and to measure the magnetic sensitivity at low temperatures. All AMR sensors exhibited suitable sensitivities to the magnetic field around the SRF cavity. The variation in these sensitivities in all AMR sensors was ~1%. Further, the AMR sensors were found to have sufficient sensitivity for mapping the magnetic field around the exterior surface of the SRF cavity.

43 PARTICLE ACCELERATORS↗

GradDFT. A software library for machine learning enhanced density functional theory

Density functional theory (DFT) stands as a cornerstone method in computational quantum chemistry and materials science due to its remarkable versatility and scalability. Yet, it suffers from limitations in accuracy, particularly when dealing with strongly correlated systems. To address these shortcomings, recent work has begun to explore how machine learning can expand the capabilities of DFT: an endeavor with many open questions and technical challenges. In this work, we present GradDFT a fully differentiable JAX-based DFT library, enabling quick prototyping and experimentation with machine learning-enhanced exchange–correlation energy functionals. GradDFT employs a pioneering parametrization of exchange–correlation functionals constructed using a weighted sum of energy densities, where the weights are determined using neural networks. Moreover, GradDFT encompasses a comprehensive suite of auxiliary functions, notably featuring a just-in-time compilable and fully differentiable self-consistent iterative procedure. To support training and benchmarking efforts, we additionally compile a curated dataset of experimental dissociation energies of dimers, half of which contain transition metal atoms characterized by strong electronic correlations. The software library is tested against experimental results to study the generalization capabilities of a neural functional across potential energy surfaces and atomic species, as well as the effect of training data noise on the resulting model accuracy.

Chemistry↗

Towards 50 Year Lifetime PV Modules: Double Glass vs. Glass/Backsheet

This award aims to increase the lifetime of c-Si modules by lowering the power degradation rate to the goal of 0.2 %/year, while also increasing the harvested irradiance per module using bifacial cells to achieve the 2030 SunShot goal of $0.03/kWh. Increasing the durability and lifetime of modules requires improved module packaging material choices and module architectures to exploit new cell improvements. Bifacial PERC cells (with backside aluminum fingers) are becoming a primary PV technology due to their decreased rear surface recombination and increased light absorption compared to traditional monofacial Al-BSF cells. Bifacial modules with double glass architectures have been deployed to capture the rear-side irradiance thereby increasing the light captured. The choice of a double glass (DG) or glass/backsheet (GB) module leads to two very different chemical (e.g., O 2 , H 2 O) and mechanical environments (e.g., mechanical stress levels) inside the PV module that impact the cell’s operational conditions. The recipient will fabricate 4-cell DG and GB minimodules with various module architectures or constructions (mono- & bifacial cells, transparent & white encapsulants), and conduct stepwise accelerated exposures and characterization.

14 SOLAR ENERGY↗

Next Generation Co-Molded One-Piece Automotive Parts

The purpose of this project was to determine the feasibility of co-molding Class A Sheet Molding Compound (SMC), structural SMC, and continuous fiber prepreg materials to produce a single piece co-molded automotive part, such as a hood. The combination of the three molding materials and the incorporation of selective design features (ribs, flanges, corrugations) was expected to eliminate the need for inner reinforcement panels, significantly reducing tooling costs and simplifying the manufacturing process. A multi-material solution would also result in significant weight savings. The scope of this project included the material characterization of the three materials, development of cure and flow simulation models, and validation of the models against parts molded with different combinations of the materials on an 11”x11” plaque tool with rib features. The intent of this project was to apply the learnings obtained from co-molding a part with simplified geometry to a Phase 2 project that would produce a single piece hood, co-molded with the same materials. Resins from INEOS Composites were provided to IDI to produce SMC and continuous fiber prepregs. Purdue University and INEOS Composites characterized the rheological and curing behavior of the different materials as well as the mechanical properties of the co-molded parts. This data was used by Purdue University to create simulation models. Models were created to predict both flow patterns and predict mechanical properties of different laminate constructions in and around the ribs. Michigan State University - Corktown validated Purdue’s models by co-molding the three materials in different combinations on a tool containing rib features provided by Century Tool. The co-molded parts were evaluated against the simulation models by Purdue, Corktown and INEOS. The project team demonstrated the following: • The ability to obtain a cohesive co-molded structure made up of Class A SMC, Structural SMC, and continuous fiber prepreg. • The ability to obtain a co-molded part with a Class A surface • Modeling of flow and fiber orientation • Modeling to predict mechanical properties of multi-material co-molded structures The predicted flow behavior and fiber orientation from simulations were then compared with the molded samples. Exact local orientation state was difficult to compare between microscopy and flow simulation, but captured general trends. Multi-material flow behavior was generally modeled well with SMC materials, but the introduction of woven material sheets requires further model development. Purdue compared the predicted versus actual mechanical properties, and INEOS Composites evaluated the surface appearance of unpainted and painted co-molded parts. The models developed by Purdue demonstrated that the mechanical properties can be predicted for parts with varying material configurations. The resulting models can be applied to future co-molded part design, tooling design, and molding conditions.

36 MATERIALS SCIENCE↗

Elucidation of Ultrafast Decay, Vibrational Beating, and Slow Decay Processes of Excited Azulene

Azulene’s nonradiative decay dynamics and kinetics from its singlet excited-states were studied using mixed-reference spin-flip time-dependent density functional theory (MRSF-TDDFT) combined with trajectory surface-hopping nonadiabatic molecular dynamics (NAMD) and Fermi’s golden rule (FGR) rate theory. The NAMD dynamics reproduce experimental observations that the S 1 → S 0 decay is accelerated and exhibits a crossover from mono- to biexponential kinetics with increasing excess vibrational energy. Minimum-energy-path analyses reveal a continuous S 1 /S 0 conical-intersection seam slightly above the S 1 minimum, providing readily accessible funnels. Time-resolved normal-mode projections reveal selective energy funneling into C–C stretching modes at 1272, 1528, and 1692 cm –1 . Constructive combinations of the latter two modes appear to promote rapid access to the conical-intersection seam, whereas their beating at ∼ 160 fs imposes a natural limit on the decrease of the decay time upon a further increase of excess energies, suggesting that their interference delays the decay. Here, the FGR rate calculation data for the S 1 → S 0 transition reaffirm that the nonradiative decay proceeds mainly near or through the conical intersection, rather than via simple nonadiabatic derivative coupling around the minimum of S 1 . On the other hand, the FGR rates indicate that S 2 decays predominantly to S 0 , as the S 2 –S 1 vibronic coupling is exceptionally weak, which serves as a primary cause for azulene’s characteristic anti-Kasha emission.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Biomimetic Rebuilding of Multifunctional Red Blood Cells: Modular Design Using Functional Components

The design and synthesis of artificial materials that mimic the structures, mechanical properties, and ultimately functionalities of biological cells remains a current holy grail of materials science. Here based on a silica cell bio-replication approach, we report the design and construction of synthetic rebuilt red blood cells (RRBCs) that fully mimic the broad properties of native RBCs: size, biconcave shape, deformability, oxygen carrying capacity, long circulation time. Four successive nano-scale processing steps: RBC bioreplication, layer-by-layer polymer deposition, and precision silica etching, followed by RBC ghost membrane vesicles fusion are employed for RRBC construction. A panel of physicochemical analyses including zeta potential measurement, fluorescence microscopy, and antibody-mediated agglutination assay proved the recapitulation of RBC shape, size, and membrane structure. Flow-based deformation studies carried out in a microfluidic blood capillary model confirrned the ability of RRBCs to deform and pass through small slits and reconstitute themselves in a manner comparable to native RBCs. Circulation studies of RRBCs conducted ex-ovo in a chick embryo and in vivo in a mouse model demonstrated the requirement of both deformability and native cell membrane surface to achieve long-term circulation. To confer additional non-native functionalities to RRBCs, we developed modular procedures in which to load functional cargos such as hemoglobin, drugs, magnetic nanoparticles, and ATP biosensors within the RRBC interior to enable various functions, including oxygen delivery, therapeutic drug delivery, magnetic manipulation, and toxin biosensing and detection. Taken together, RRBCs represent a class of long circulating RBC-inspired artificial hybrid materials with a broad range of potential applications.

59 BASIC BIOLOGICAL SCIENCES↗

Unraveling spin entanglement using quantum gates with scanning tunneling microscopy-driven electron spin resonance

Quantum entanglement is a fundamental resource for quantum information processing, and its controlled generation and detection remain key challenges in scalable quantum architectures. Here, we numerically demonstrate the deterministic generation of entangled spin states in a solid-state platform by implementing quantum gates via electron spin resonance combined with scanning tunneling microscopy (ESR-STM). Using two titanium atoms on a MgO/Ag(100) substrate as a model, we construct a two-qubit system whose dynamics are coherently manipulated through tailored microwave pulse sequences. We generate Bell states by implementing a Hadamard gate followed by a controlled-NOT gate, and evaluate its fidelity and concurrence using the quantum-master equation-based code TimeESR. Our results demonstrate that ESR-STM can create entangled states with significant fidelity. This study paves the way for the realization of atom-based quantum circuits and highlights ESR-STM as a powerful tool for probing and engineering entangled states on surfaces.

Switzer, Eric D. [Donostia International Physics C↗

J-PLUS: Support vector regression to measure stellar parameters

Stellar parameters are among the most important characteristics in studies of stars which, in traditional methods, are based on atmosphere models. However, time, cost, and brightness limits restrain the efficiency of spectral observations. The Javalambre Photometric Local Universe Survey (J-PLUS) is an observational campaign that aims to obtain photometry in 12 bands. Owing to its characteristics, J-PLUS data have become a valuable resource for studies of stars. Machine learning provides powerful tools for efficiently analyzing large data sets, such as the one from J-PLUS, and enables us to expand the research domain to stellar parameters. The main goal of this study is to construct a support vector regression (SVR) algorithm to estimate stellar parameters of the stars in the first data release of the J-PLUS observational campaign. The training data for the parameter's regressions are featured with 12-waveband photometry from J-PLUS and are crossidentified with spectrum-based catalogs. These catalogs are from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope, the Apache Point Observatory Galactic Evolution Experiment, and the Sloan Extension for Galactic Understanding and Exploration. We then label them with the stellar effective temperature, the surface gravity, and the metallicity. Ten percent of the sample is held out to apply a blind test. We develop a new method, a multi-model approach, in order to fully take into account, the uncertainties of both the magnitudes and the stellar parameters. The method utilizes more than 200 models to apply the uncertainty analysis. We present a catalog of 2 493 424 stars with the root mean square error of 160 K in the effective temperature regression, 0.35 in the surface gravity regression, and 0.25 in the metallicity regression. We also discuss the advantages of this multi-model approach and compare it to other machine-learning methods.

79 ASTRONOMY AND ASTROPHYSICS↗

Removal of radon progeny from delicate surfaces

210 Po α-decay-driven neutron background is a concern for many rare event search experiments. It is a difficult to control background because its radiogenic component depends on the air exposure history of parts. In this study, we demonstrate that about half of the radon progeny 210 Po can be removed from copper and silicon surfaces relatively easily by wiping a copper sample with acetone-wetted tissue and a silicon detector with acetone soaked cotton balls. For a copper sample, we demonstrate that long-lived 210 Pb is removed with similar effectiveness. For copper, allocated the longest counting time, additional wiping was found to be largely ineffective. For silicon, the removal effectiveness has large uncertainties. Additional cleaning showed a small but statistically significant effect. Here, capitalizing on this trivial cleaning step will allow experiments to relax their requirements on the allowable air exposure time during construction, leading to cost and time savings.

Radioactivity↗

Imaging of Calibrated Defects in Additively Manufactured Materials: Pulsed Thermal Tomography Nondestructive Examination of Additively Manufactured Reactor Materials and Components

Additive manufacturing (AM, or 3D printing) for commercial nuclear energy applications is an emerging method for cost-efficient manufacturing aimed at replacing aging nuclear reactor parts and reducing costs for new construction. Known material flaws in AM include low-density regions consisting of non-sintered powder, which have to be detected to ensure the safety of long-term performance nuclear reactor components. Currently, limited options are available for nondestructive evaluation (NDE), either during or post manufacturing. As a solution to NDE of AM, we are developing pulsed thermal imaging which is non-contact, one-sided, and scalable to arbitrary size and shape of the AM parts. Pulsed thermography (PT) system utilizes a high intensity flash lamp to rapidly heat surface of sample, and a high sensitivity fast-frame megapixel infrared (IR) camera to capture data of surface temperature variations. The acquired data cube consists of a stack of surface temperature images taken at different times. Information about material internal defects is extracted by analyzing the data cube. This report provides results of preliminary performance evaluation of pulsed thermal imaging capability in detection of imprinted flaws in AM metallic structures. The flaws were introduced into AM parts as imprinted hemispherical low density regions, consisting of trapped un-sintered metallic powder. Specimens for developed for this study consisted of AM stainless steel 316 and Inconel 718 plates. The diameters of imprinted defects varied from 1mm to 8mm, and their depths below the plate flat surface varied between 1mm and 6mm. Pulsed thermal tomography (PTT) processes the measured data cube to obtain 3D reconstructions of material effusivity using a unique inversion algorithm developed at Argonne. PTT has been previously used in imaging of similar size flat bottom hole (FBH) simulated defects in stainless steel 316 and Inconel 718 specimens. In the study involving AM specimens, PTT imaging results have shown that 1mm-diameter defects located 1mm and 2mm below the surface of specimens were detectable. Larger size defects were detectable at greater depth. We also explored an alternative approach to detection of material flaws in PT data cube, which is using neural learning-based approach to blind source separation. Detection of small material defects requires finding features in the data cube which have signal contrast levels approaching sensitivity limit of IR camera. In this study, an optimized Neural Learning based Blind Source Separation (NLBSS) algorithm, including Principal Component Analysis (PCA), and Independent Component Analysis (ICA) is demonstrated to automatically extract principal temporal and spatial features of thermography frames to enhance flaw detection. By using the NLBSS algorithm, material internal defects can be automatically detected. Furthermore, this processing approach compensates for experimental thermal imaging artifacts, such as noise and uneven heating. By merging artificial intelligence with phtotothermics, the NDE system detects internal calibrated defects of various sizes and depths in AM nuclear-grade metallic alloys.

36 MATERIALS SCIENCE↗

Complete Performance Comparison Between the Optimized Image Construction Algorithm (U-MBIR) and the Existing Reconstruction Algorithm for Detecting Defects and Damage in Concrete

Reinforced concrete (RC) is a composite material subjected to mechanical, thermal, and chemical loads throughout its service life. Because of these external stressors and the susceptibility of RC structural members to shrinkage and microcracking, the material degrades throughout its life cycle. This deterioration can lead to a decrease in member capacity and, ultimately, poses a threat to the structural integrity. Thus, it is crucial that the damage caused by aging and degradation be monitored and assessed at regular intervals throughout the material’s service life. Since coring of the material is typically not feasible for in-service structural systems, non-destructive evaluation (NDE) methodologies are used to assess remaining structural capacity. NDE methods enable surface and subsurface examination without damaging or degrading the medium. Moreover, RC is a critical component of nuclear power plants; thus, its safety and reliability must be thoroughly examined throughout the life cycle of the structural system. Ultrasonic measurements have been an industry standard for both surface and subsurface inspections. To this end, Oak Ridge National Laboratory (ORNL) has researched and developed advanced image reconstruction algorithms to capture internal damage. The results and discussion presented herein summarize the current state of the ultrasonic model–based iterative reconstruction (U-MBIR) algorithm developed at ORNL. More specifically, this report presents a comparison between reconstruction images produced via a widely employed ultrasonic NDE technique—the synthetic aperture focusing technique (SAFT)—and the ORNL-developed U-MBIR algorithm. These NDE methodologies are demonstrated using ultrasonic data collected from four concrete specimens. Overall, the U-MBIR algorithm eliminates artifacts and noise that are typically present within the SAFT reconstructions, and it shows defects and anomalies more clearly than the SAFT images. In conclusion, this algorithm is suitable for identifying concrete defects, although more improvements and optimization could be done to better define internal defects.

36 MATERIALS SCIENCE↗

Neural Density Estimation and Uncertainty Quantification for ChemCam Spectra [Slides]

The ChemCam instrument of Curiosity uses laser-induced breakdown spectroscopy (LIBS). It fires a laser at target and vaporizes rock surfaces, creating a plasma. Three spectrographs divide the plasma light into wavelengths for chemical analysis: ultraviolet, violet, and visible near-infrared. Regression methods (SVR, PCR, CNN) have been employed for calibration (prediction of the elemental composition of samples); however, labeled ChemCam samples are limited. Here, we focus on unsupervised learning and employ generative models from ChemCam analysis. Further, we use labels (supervised) in combination to the generative model to compute uncertainties related to predictions. We report generative modeling can be successfully applied to model real-world data. Normalizing flow models can be efficiently constructed on latent spaces for fast downstream inference. Unsupervised and supervised learning can be combined to form an uncertainty quantification framework.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗