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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Flame-retardant cellulose-aerogel composite from agriculture waste for building insulation

Bio-based thermal insulation materials are in high demand due to their availability, reproducibility, and carbon-sequestration nature. However, high flammability, moisture condensation, and high thermal conductivity of biogenic material are major concerns for sustainable building applications. In this study, we report the fire-retardant cellulose aerogel insulation nanocomposites derived from wheat straw and silica aerogel, in which sodium bicarbonate improves its fire retardancy. We combined blended straw fibers and hammermilled straw fibers to create a structural hierarchy composite. The blended straw, with its longer and thicker size, served as reinforcement, while the hammermilled straw fibers acted as filler. This hierarchical structure was further integrated with aerogel for applications in green buildings. The as-prepared materials show a low thermal conductivity of 24.1 mW/m.K, high flexural modulus of 736 MPa, hydrophobicity with a water contact angle of 110.42°, and excellent fire retardancy. Altogether, this work provides an effective method for the synthesis of fire-retardant biogenic thermal insulation materials and shows a promising way for next-generation bio-based insulation materials.

36 MATERIALS SCIENCE↗
DOE OSTI2022

AI-Enhanced Co-Design for Next-Generation Microelectronics: Innovating Innovation [Workshop Report]

In April 5-7, 2022, Sandia National Laboratories hosted a second virtual workshop to further explore the potential for developing AI-enhanced co-design for microelectronics (AICoM). This second piece in an ongoing workshop series again brought together two themes. The first theme, co-design for next generation microelectronics, was drawn from the 2018 Department of Energy Office of Science (DOE SC) “Basic Research Needs for Microelectronics” (BRN) report (DOE/SC, 2018, 2021), which called for a “fundamental rethinking” of the traditional design approach to microelectronics, in which subject matter experts (SMEs) in each microelectronics discipline (materials, devices, circuits, algorithms, etc.) work near-independently. Instead, the BRN called for a non-hierarchical, egalitarian vision of co-design, wherein “each scientific discipline informs and engages the others” in “parallel but intimately networked efforts to create radically new capabilities.” The second theme, exploiting and advancing artificial intelligence (AI) to support co-design for microelectronics, acknowledges the continuing breakthroughs in AI that are currently enhancing and accelerating solutions to traditional design problems in materials synthesis and processing, circuit design, and electronic design automation (EDA).

42 ENGINEERING↗
DOE OSTI2020

Low Temperature Aggregation Transitions in N3 and Y6 Acceptors Enable Double‐Annealing Method That Yields Hierarchical Morphology and Superior Efficiency in Nonfullerene Organic Solar Cells

Abstract Thermal transition of organic solar cells (OSCs) constituent materials are often insufficiently researched, resulting in trial‐and‐error rather than rational approaches to annealing strategies to improve domain purity to enhance the power conversion efficiency. Despite the potential utility, little is known about the thermal transitions of the modern high‐performance acceptors Y6 and N3. Here, by using an optical method, it is discovered that the acceptor N3 has a clear solid‐state aggregation transition at 82 °C. This unusually low transition not only explains prior optimization protocols, but the transition informs and enables a double‐annealing method that can fine‐tune aggregation and the device morphology. Compared with 16.6% efficiency for PM6:N3:PC 71 BM control devices, higher efficiency of 17.6% is obtained through the improved protocol. Morphology characterization with x‐ray scattering methods reveals the formation of a multilength scale morphology. Moreover, the double‐annealing method is illustrated and easily transferred and validated with Y6‐based devices, using the transition of Y6 at 102 °C. As a result, the PCE improved from 16.0% to 16.8%. Design of high‐performance acceptors with yet lower aggregation transitions might be required for OSCs to successfully transition to low thermal budget industrial processing methods where annealing temperatures on plastic substrates have to be kept low.

Qin, Yunpeng↗
DOE OSTI2024

Resonant Soft X-ray Scattering Reveals Hierarchical Structure in a Multicomponent Vapor-Deposited Glass

Multiphase vapor-deposited glasses are an important class of materials for organic electronics, particularly organic photovoltaics and thermoelectrics. These blends are frequently regarded as molecular alloys and there have been few studies of their structure at nanometer scales. Here, in this work, we show that a codeposited system of TPD (N,N'-bis(3-methylphenyl)-N,N'-diphenylbenzidine) and DO37 (disperse orange 37), two small molecule glass-formers, separates into amorphous, compositionally distinct phases with a domain size and spacing ca. 10s of nanometers that depends on substrate temperature during deposition. Domains rich in one of the two components become larger and more pure at higher deposition temperatures. We use resonant soft X-ray scattering (RSoXS) complemented with atomic force microscopy (AFM) and photoinduced force microscopy to measure the phase separation, topography, and purity of the deposited films. A forward-simulation approach to RSoXS analysis, the National Institute of Standards and Technology RSoXS Simulation Suite (NIST RSoXS simulation suite), is used with models developed from AFM images to evaluate the energy dependence of scattering across multiple length scales and interpret the RSoXS with respect to structure within the films. We find that the RSoXS is sensitive to a length scale of phase separation buried within the film that is consistent with the surface composition profile, and correlates to the topography to an extent that depends on substrate temperature. We demonstrate that vacuum scattering, which is often ignored in RSoXS analysis, contributes significantly to the features and energy dependence of the RSoXS pattern, and then illustrate how to properly account for vacuum scattering to analyze films with significant roughness. We then use this analysis framework to understand structure development mechanisms that occur during vapor deposition of a TPD-DO37 codeposited glass with results that outline paths to tune morphology in multicomponent materials.

36 MATERIALS SCIENCE↗
DOE OSTI2025

High-emissivity, thermally robust emitters for high power density thermophotovoltaics

Thermal radiative energy transport is essential for high-temperature energy harvesting technologies, including thermophotovoltaics (TPVs) and grid-scale thermal energy storage. However, the inherently low emissivity of conventional high-temperature materials constrains radiative energy transfer, thereby limiting system performance and technoeconomic viability. Here, in this study, we demonstrate ultrafast femtosecond laser-material interactions to transform diverse materials into near-blackbody surfaces with broadband spectral emissivity above 0.96. This enhancement arises from hierarchically engineered light-trapping microstructures enriched with nanoscale features, effectively decoupling surface optical properties from bulk thermomechanical properties. These laser-blackened surfaces (LaBS) exhibit exceptional thermal stability, retaining high emissivity for over 100 h at temperatures exceeding 1,000°C, even in oxidizing environments. When applied as TPV thermal emitters, Ta LaBS double electrical power output from 2.19 to 4.10 W cm −2 at 2,200°C while sustaining TPV conversion efficiencies above 30%. This versatile, largely material-independent technique offers a scalable and economically viable pathway to enhance emissivity for advanced thermal energy applications.

laser-blackened surfaces↗
DOE OSTI2020

Exploring physics of ferroelectric domain walls via Bayesian analysis of atomically resolved STEM data

The physics of ferroelectric domain walls is explored using the Bayesian inference analysis of atomically resolved STEM data. We demonstrate that domain wall profile shapes are ultimately sensitive to the nature of the order parameter in the material, including the functional form of Ginzburg-Landau-Devonshire expansion, and numerical value of the corresponding parameters. The preexisting materials knowledge naturally folds in the Bayesian framework in the form of prior distributions, with the different order parameters forming competing (or hierarchical) models. Here, we explore the physics of the ferroelectric domain walls in BiFeO 3 using this method, and derive the posterior estimates of relevant parameters. More generally, this inference approach both allows learning materials physics from experimental data with associated uncertainty quantification, and establishing guidelines for instrumental development answering questions on what resolution and information limits are necessary for reliable observation of specific physical mechanisms of interest.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRSDate not supplied

Macro-scale Testing and Micromechanics Modeling of Fracture Behaviors for Boron Carbide Composites with Hierarchical Microstructures

With a unique combination of properties including high hardness, low density, wear and corrosion resistance, thermal stability, high neutron absorption, and semi-conductivity, boron carbide (B4C) is a candidate material for various engineering applications that involve extreme environment. The current applications of boron carbide, however, are limited by its intrinsic brittleness due to its strong covalent bonding. To toughen boron carbide, in this work hierarchical microstructure designs was used to provide multiple toughening mechanisms including crack deflection/bridging, micro-crack toughening, etc. Using field assisted sintering, B4C composites with hierarchical microstructure features including graphite platelets, micron and sub-micron sized TiB2 reinforcements were fabricated. The fracture toughness of fabricated B4C composites were previously measured at micro-scale using micro-indentation followed by post-testing microstructure inspection. However, questions including whether the fracture toughness enhancement measured at micro-scale can translate to macro-scale mechanical properties, and what are the fundamental mechanisms behind observed fracture toughness enhancement, remain to be answered. In this study, the fabricated B4C composites were tested using standardized four-point bending method to obtain fracture toughness at macro-scale. In addition, micromechanics modeling was conducted using MAC/GMC code and crack-band model to study the effect of residual stress and weak interphases on fracture behaviors of B4C composites reinforced with TiB2 particles. Through standardized four-point bending tests, fracture toughness enhancements up to 2.85, 3.32, and 3.65 MPa∙m(exp 1/2) (from 2.38 MPa∙m(exp 1/2)) were achieved for B4C composites with graphite platelets addition (micro/nano B4C), with TiB2 formation (micro B4C-TiB2), and with both graphite and TiB2 addition (micro/nano B4C-TiB2) respectively. Micromechanics modeling indicated that introduction of thermal residual stress and weak interphases caused enhanced micro-cracking behavior and resulted in the observed fracture toughness enhancement. These results furthered understanding about the mechanical behaviors at macro-scale and the mechanisms behind observed fracture toughness enhancement for B4C composites with hierarchical microstructures and can provide reference data for the future design of B4C composites with optimized microstructures for further fracture toughness enhancement.

Boron Carbide↗
DOE OSTI2024

Random forest prediction of crystal structure from electron diffraction patterns incorporating multiple scattering

Diffraction is the most common method to solve for unknown or partially known crystal structures. However, it remains a challenge to determine the crystal structure of a new material that may have nanoscale size or heterogeneities. Here, in this study, we train an architecture of hierarchical random forest models capable of predicting the crystal system, space group, and lattice parameters from one or more unknown two-dimensional electron diffraction patterns. Our initial model correctly identifies the crystal system of a simulated electron diffraction pattern from a 20-nm-thick specimen of arbitrary orientation 67% of the time. We achieve a topline accuracy of 79% when aggregating predictions from ten patterns of the same material but different zone axes. The space group and lattice predictions range from 70% to 90% accuracy and median errors of 0.01-0.5Å, respectively, for cubic, hexagonal, trigonal, and tetragonal crystal systems while being less reliable on orthorhombic and monoclinic systems. We apply this architecture to a four-dimensional scanning transmission electron microscopy scan of gold nanoparticles, where it accurately predicts the crystal structure and lattice constants. These random forest models can be used to significantly accelerate the analysis of electron diffraction patterns, particularly in the case of unknown crystal structures. Additionally, due to the speed of inference, these models could be integrated into live transmission electron microscopy experiments, allowing real-Time labeling of a specimen.

36 MATERIALS SCIENCE↗
NASA NTRSDate not supplied

Passive Dust Mitigation Technologies Being Developed for Demonstration Under Patch Plate Materials Compatibility Analysis Task

With the Artemis program, we are planning longer stays on the surface, with more activities that have the potential to put the astronauts and equipment in contact with greater quantities of lunar dust. The success of these missions will depend on our understanding of material interactions with lunar dust and the development of ways to mitigate dust effects in cases where exposure to dust will lead to failure of components, unacceptable loss of power or thermal control, unacceptable loss of visibility, or health issues. Passive dust mitigation by coating or surface alteration is one method that is being developed and demonstrated under the Space Technology Mission Directorate’s Game Changing Technology, Dust Mitigation Program as part of the Patch Plate Materials Compatibility Assessment Task. The goal of the task is to alter the surfaces of materials in order to passively reduce the adhesion of dust, demonstrate their performance in relevant ground-based tests using lunar simulants, and prepare them for demonstration through experiment on the lunar surface. Optically transparent, sputter deposited, work function matching coatings are being developed to reduce adhesion of dust to windows, lenses and display panels by matching the minimum energy to remove an electron from the surface to that of lunar dust in order to reduce adhesion due to charge transfer. Low surface energy coatings and surfaces for thermal control are also being developed to reduce the bonding of dust with the surface enabling it to be removed more easily. Conductive coatings with the ability to shed dust more easily are being developed for use with the active Electrodynamic Dust Shield technology to help reduce the power needed to remove dust from the surface. Passive dust mitigation surfaces for metals such as aluminum, stainless steel, and titanium are being developed that reduce the area of dust contact with the surfaces through topographical modification using laser ablation patterning to impart hierarchical topographies with nanometer to micrometer length scales in a single step. Topographically modified polymeric materials, both those with extensive space heritage and those with lower technology readiness levels, are also being evaluated. Space suit fabric surfaces that can reduce dust penetration into and through the fabric are also being investigated as well as pristine and topographically modified ceramic materials that exhibit high wear resilience. An overview of the passive dust mitigation surfaces and coatings being developed under this task, ground testing being conducted using lunar simulants, characterization techniques, and materials preparation for flight sample delivery for integration into the Alpha Space Regolith Adherence Characterization experiment going to the lunar surface on a Commercial Lunar Payload Services (CLPS) lander in 2023 will be discussed.

Lunar dust, passive mitigation, lunar simulant, co↗
NASA NTRS1999

Analysis of Composite Panels Subjected to Thermo-Mechanical Loads

The results of a detailed study of the effect of cutout on the nonlinear response of curved unstiffened panels are presented. The panels are subjected to combined temperature gradient through-the-thickness combined with pressure loading and edge shortening or edge shear. The analysis is based on a first-order, shear deformation, Sanders-Budiansky-type shell theory with the effects of large displacements, moderate rotations, transverse shear deformation, and laminated anisotropic material behavior included. A mixed formulation is used with the fundamental unknowns consisting of the generalized displacements and the stress resultants of the panel. The nonlinear displacements, strain energy, principal strains, transverse shear stresses, transverse shear strain energy density, and their hierarchical sensitivity coefficients are evaluated. The hierarchical sensitivity coefficients measure the sensitivity of the nonlinear response to variations in the panel parameters, as well as in the material properties of the individual layers. Numerical results are presented for cylindrical panels and show the effects of variations in the loading and the size of the cutout on the global and local response quantities as well as their sensitivity to changes in the various panel, layer, and micromechanical parameters.

Noor, Ahmed K.↗
DOE OSTI2026

Reversible Polymer–Metal Mechanical Transitions Enabled by Electrochemical Modulation

The mechanical properties of materials, e.g., elastic modulus and hardness, are important for many engineering applications. Here, we introduce a hierarchically ordered structure (HOS) polymer system that exhibits exceptionally large, reversible changes in mechanical behavior upon electrochemical lithiation and delithiation. Full lithiation of the HOS polymer induces a substantial mechanical transition from polymer-like to metal-like attributes, yielding a 10-fold increase in elastic modulus and a 3-fold increase in hardness, with values comparable to those of aluminum. Upon removal of Li+ ions, the elastic modulus and hardness return to nearly pristine polymer levels, and this transformation remains highly repeatable over many electrochemical cycles. Reversible transitions between polymer-like and metal-like mechanical behaviors offer a new pathway for engineering materials for applications that require tunable mechanical properties, such as soft robotics and stimuli-responsive systems.

Thapa, Santosh↗
DOE OSTI2021

In Situ Wood Delignification toward Sustainable Applications

As one of the most abundant and versatile natural materials on Earth, recently wood has attracted tremendous attention from scientists and engineers due to its outstanding advantages, including hierarchically porous microstructure, high mechanical strength, environmental friendliness, renewability, and biodegradability. Wood’s hierarchically porous structure and chemical components (e.g., cellulose, hemicelluloses, and lignin) enable its mechanical, ionic, optical, and thermal properties to be tuned via physical, chemical, and/or thermal modifications. Among these various approaches, the chemical delignification of bulk wood is the most fascinating, in which the majority of lignin and hemicelluloses is removed while leaving the cellulose intact, maintaining wood’s physical integrity and hierarchical structure. This delignified structure is unique, composed of hollow, aligned channels made up of cellulose microfibrils, and particularly attractive given its origin from a sustainable and renewable resource. As a result, delignified wood has attracted increasing attention for applications that go far beyond traditional wood utilization, such as lightweight yet strong structural materials, energy storage and conversion, environmental remediation, flexible electronics, and bioengineering. Here, we review recent developments in bulk wood delignification strategies toward the achievement of such advanced wood technologies for sustainable applications, with a focus on the research in our group. Similar to chemical pulping and bleaching, wood delignification involves a series of nucleophilic reactions based on alkaline Na 2 SO 3 or Na 2 S systems (i.e., chemical pulping) or electrophilic, radical, and oxidation reactions based on H 2 O 2 , ClO 2 , or NaClO systems (i.e., chemical bleaching) to deconstruct, fragment, and promote the hydrophilicity of lignin macromolecules, which finally make lignin easier to be removed. We discuss the structure and properties of partially and near-completely delignified wood, with a focus on process-structure–property relationships. The resulting delignified wood materials, with tunable structure and properties, demonstrate various advanced functions, in a wide range of advanced applications, such as building and construction, green energy, and electronics. Finally, the potential challenges and appealing perspectives of in situ wood delignification are discussed. In situ wood delignification, as a powerful modification strategy, has speeded up the development of advanced wood technologies and wood-based functional materials and products.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Hierarchical Polyelemental Nanoparticles as Bifunctional Catalysts for Oxygen Evolution and Reduction Reactions

Efficient electrocatalysts are critical in various clean energy conversion and storage systems. Polyelemental nanomaterials are attractive as multi-functional catalysts due to their wide compositions and synergistic properties. However, controlled synthesis of polyelemental nanomaterials is difficult due to their complex composition. Herein, we present a one-step synthetic strategy to fabricate a hierarchical polyelemental nanomaterial, which contains ultrasmall precious metal nanoparticles (IrPt, ~5 nm) anchored on spinel-structure transition metal oxide nanoparticles. The polyelemental nanoparticles serve as excellent bifunctional catalysts for the oxygen evolution reaction (OER) and oxygen reduction reaction (ORR). The mass catalytic activity of the polyelemental nanoparticles is 7-times higher than that of Pt in ORR and 28-times that of Ir in OER at the same overpotentials, demonstrating the high activity of the bifunctional electrocatalyst. We attribute this outstanding performance to the controlled multiple elemental composition, mixed chemical states, and large electroactive surface area. The hierarchical nanostructure and polyelemental design of these nanoparticles offers a general and powerful alternative material for catalysis, solar cells, and more.

25 ENERGY STORAGE↗
DOE OSTI2022

Porous Organic Cages CC3 and CC2 as Adsorbents for the Separation of Carbon Dioxide from Nitrogen and Hydrogen

The selective capture of carbon dioxide over nitrogen and hydrogen is of great industrial interest in flue gas and hydrogen purification respectively. Microporous adsorbents are highly suitable materials to preferentially adsorb gases. Here, in particular, Porous organic cages (POCs) with tunable hierarchically ordered micropores, high surface area, and thermodynamic affinity for CO 2 make them appealing candidates for these applications. Herein, we demonstrate that two prototypical POCs denoted as CC3, and CC2 with limiting pore aperture of 3.6 Å can selectively separate CO 2 from N 2 , and H 2 . For CC3 adsorption selectivities as high as ~ 8 and ~20 for CO 2 /N 2 and CO 2 /H 2 respectively were observed. For CC2 adsorption selectivities as high as ~ 9 and ~35 for CO 2 /N 2 and CO 2 /H 2 respectively were observed. Interestingly, the adsorption selectivity of the studied gases correlated linearly with polarizability selectivity.

Carbon Dioxide↗
DOE OSTI2021

Impact of Controlled Storage Conditions on the Hydrolysis and Surface Morphology of Amorphous-UO 3

The hydration and morphological effects of amorphous (A)-UO 3 following storage under varying temperature and relative humidity have been investigated. This study provides valuable insight into U-oxide speciation following aging, the U-oxide quantitative morphological data set, and, overall, the characterization of nuclear material provenance. A-UO 3 was synthesized via the washed uranyl peroxide synthetic route and aged based on a 3-factor circumscribed central composite design of experiment. Target aging times include 2.57, 7.00, 14.0, 21.0, and 25.4 days, temperatures of 5.51, 15.0, 30.0, 45.0, and 54.5 °C, and relative humidities of 14.2, 30.0, 55.0, 80.0, and 95.8% were examined. Following aging, crystallographic changes were quantified via powder X-ray diffraction and an internal standard Rietveld refinement method was used to confirm the hydration of A-UO 3 to crystalline schoepite phases. The particle morphology from scanning electron microscopy images was quantified using both the Morphological Analysis of MAterials software and machine learning. Results from the machine learning were processed via agglomerative hierarchical clustering analysis to distinguish trends in morphological attributes from the aging study. Significantly hydrated samples were found to have a much larger, plate-like morphology in comparison to the unaged controls. Predictive modeling via a response surface methodology determined that while aging time, temperature, and relative humidity all have a quantifiable effect on A-UO 3 crystallographic and morphological changes, relative humidity has the most significant impact.

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

Hierarchical Biogenic-Based Thermal Insulation Foam

Biogenic-based foam, renowned for its sustainable and eco-friendly properties, is emerging as a promising thermal insulating material with the potential to significantly enhance energy efficiency and sustainability in building applications. However, its relatively high thermal conductivity, large-pore configurations, and energy-intensive manufacturing processes hinder its widespread use. Here, we report on the scalable, one-pot synthesis of biogenic foams achieved by integrating recycled paper pulp and in situ nanoporous silica formation, resulting in a hierarchical structure comprising both micropores and nanopores. Ambient solvent-exchange drying can preserve the pore structure by reducing the capillary forces during the drying process. The resulting flame-retardant and hydrophobic foam exhibits low density (0.110 g/cm 3 ), ideal porosity (70.69%), excellent thermal conductivity (0.033 W/(m·K)), and impressive compressive strength (1.48 MPa at 80% strain). Furthermore, this recyclable biogenic foam, with its hierarchical pore structure and environmental durability, shows great potential for energy-efficient building applications.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Simultaneous inference of equation of state parameters and unknown data errors with uncertainty quantification via hierarchical Bayesian posterior maximization

Equations of state (EOSs) are a key component in running hydrodynamic simulations as they relate the thermodynamic states for the material. The Davis reactants EOS is commonly used for modeling high explosives (HEs), and the EOS model parameters are calibrated using material specific data. The calibrations are often performed with uncertainty quantification via Bayesian inference to account for uncertainty in the data and generate ensembles of likely parameters. However, there are relatively few HE data sets to use for calibration and many are historical and lack error information. In this work, we simultaneously calibrate the Davis reactants EOS model parameters and unknown data error terms for the high explosive PBX 9501. To quantify the uncertainty in the models and the data, we use a Bayesian framework for the calibration and compute the hierarchical Bayesian posterior distribution with both a posteriori maximization approach and Markov Chain Monte Carlo. In general, we find that, given our assumptions, the two approaches result in similar calibrated parameters, posterior covariance matrices, and insights about the parameters but that the posterior maximization requires far less computational resources.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Materials representation and transfer learning for multi-property prediction

The adoption of machine learning in materials science has rapidly transformed materials property prediction. Hurdles limiting full capitalization of recent advancements in machine learning include the limited development of methods to learn the underlying interactions of multiple elements as well as the relationships among multiple properties to facilitate property prediction in new composition spaces. To address these issues, we introduce the Hierarchical Correlation Learning for Multi-property Prediction (H-CLMP) framework that seamlessly integrates: (i) prediction using only a material's composition, (ii) learning and exploitation of correlations among target properties in multi-target regression, and (iii) leveraging training data from tangential domains via generative transfer learning. The model is demonstrated for prediction of spectral optical absorption of complex metal oxides spanning 69 three-cation metal oxide composition spaces. H-CLMP accurately predicts non-linear composition-property relationships in composition spaces for which no training data are available, which broadens the purview of machine learning to the discovery of materials with exceptional properties. This achievement results from the principled integration of latent embedding learning, property correlation learning, generative transfer learning, and attention models. The best performance is obtained using H-CLMP with transfer learning [H-CLMP(T)] wherein a generative adversarial network is trained on computational density of states data and deployed in the target domain to augment prediction of optical absorption from composition. H-CLMP(T) aggregates multiple knowledge sources with a framework that is well suited for multi-target regression across the physical sciences.

36 MATERIALS SCIENCE↗