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At least 1,495 records · Page 83
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Enhancing the multi-functional properties of renewable lignin carbon fibers via defining the structure–property relationship using different biomass feedstocks

Lignin has been explored extensively as a renewable precursor for carbon materials, considering its abundance as a major component of plant cell walls and its sustainability as a byproduct of both lignocellulosic biorefinery and the paper-making industry. Despite the extensive efforts to define the process–property relationship, it remains largely unknown how lignin biosynthesis and its chemistry would impact the resultant carbon fiber properties, for both mechanical and electroconductive performances. Such inadequate understanding fundamentally limits feedstock design and selection to improve carbon fiber properties toward broader commercial applications. Using lignin from a broad range of biomass feedstocks for carbon fiber manufacturing, we have fundamentally explored the structure–function relationship between lignin chemistry and carbon fiber performance. Specifically, lignin extracted from hardwood (sugar maple), softwood (loblolly pine and red cedar), and herbaceous plants (corn stover and switchgrass) was used for carbon fiber manufacturing, considering the very different lignin structures from these feedstocks. Linear regression models were established to define the relationship between carbon fiber mechanical properties and lignin structural characteristics. The results highlighted that the content of β-O-4 linkages correlates significantly with the tensile strength and elastic modulus of lignin carbon fibers, indicating that more linear β-O-4 linkages would promote the carbon fiber mechanical performance. Moreover, electroconductive properties are essential for broader energy device application of lignin-based carbon fibers, yet the mechanisms controlling their electroconductivity are largely unknown. Furthermore, we hereby demonstrated that a higher β-O-4 content also promotes the electroconductivity of lignin carbon fibers. Microstructure analysis further revealed that the crystallite size and content of the pre-graphitic turbostratic carbon structure in lignin-based carbon fibers were enhanced as the β-O-4 linkages increased. The content of β-O-4 linkages has shown a strong correlation with the crystallite content in a linear regression model. This study thus revealed the underlying mechanisms regarding how the lignin structure in planta defines the resultant carbon fiber properties. Moreover, the study also highlighted the correlation between the mechanical and electroconductive properties of lignin-based carbon fibers, both of which were defined by the lignin structure.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Rapid quantitative analysis of trace elements in plutonium alloys using a handheld laser-induced breakdown spectroscopy (LIBS) device coupled with chemometrics and machine learning

Here, we present the first reported quantification of trace elements in plutonium via a portable laser-induced breakdown spectroscopy (LIBS) device and demonstrate the use of chemometric analysis to enhance the handheld device's sensitivity and precision. Quantification of trace elements such as iron and nickel in plutonium metal via LIBS is a challenging problem due to the complex nature of the plutonium optical emission spectra. While rapid analysis of plutonium alloys has been demonstrated using portable LIBS devices, such as the SciAps Z300, their detection limits for trace elements are severely constrained by their achievable pulse power and length, light collection optics, and detectors. In this paper, analytical methods are evaluated as a means to circumvent the detection constraints. Three chemometric methods often used in analytical spectroscopy are evaluated; principal component regression, partial least-squares regression, and artificial neural networks. These models are evaluated based on goodness-of-fit metrics, root mean-squared error, and their achievable limits of detection (LoDs). Partial least squares proved superior for determining content of iron and nickel in plutonium metal, yielding LoDs of 15 and 20 ppm, respectively. These results of identifying the undesirable trace elements in plutonium components are critical for applications such as fabricating radioisotope thermoelectric generators or nuclear fuel.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

Computer-aided design of stability enhanced nicotinamide cofactor biomimetics for cell-free biocatalysis

Cell-free biocatalysis (CFB) is an efficient and environmentally friendly method to synthesize molecules such as pharmaceuticals, biochemicals, and biofuels through the in vitro use of enzyme cascades. These enzymes often require redox cofactors to drive chemical reactions. Natural redox cofactors (NAD(P)H) are expensive to isolate, motivating synthetic nicotinamide cofactor biomimetics (NCBs) as a cost-effective solution. A select handful of NCBs have been identified as potential NAD(P)H alternatives with comparable or improved redox capabilities, however, they display a tendency to degrade in common buffers. In this study, a library of 132 NCB candidates is systematically generated, over 85% of which have not been characterized in the literature, to expand the diversity of currently explored NCBs. The decomposition mechanism of NCBs in phosphate is evaluated using density functional theory (DFT), revealing protonation at the nicotinamide C5 position as a reporter of cofactor stability. Based on this result, we trained a linear regression model on DFT calculated descriptors to predict NCB stability in phosphate buffer, achieving mean absolute error (MAE) and root mean squared error (RMSE) values within computational accuracy. Analysis of key atomic descriptors and qualitative trends in our dataset informed the design of novel NCB candidates we propose with optimized stability. This work enables researchers to predict the relative stability of NCBs before synthesis, thereby streamlining the process to make CFB more affordable and viable at industry scales.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2013

Conduction mechanisms in thin atomic layer deposited Al{sub 2}O{sub 3} layers

Thin Al{sub 2}O{sub 3} layers of 2–135 nm thickness deposited by thermal atomic layer deposition at 80 °C were characterized regarding the current limiting mechanisms by increasing voltage ramp stress. By analyzing the j(U)-characteristics regarding ohmic injection, space charge limited current (SCLC), Schottky-emission, Fowler-Nordheim-tunneling, and Poole-Frenkel-emission, the limiting mechanisms were identified. This was performed by rearranging and plotting the data in a linear scale, such as Schottky-plot, Poole-Frenkel-plot, and Fowler-Nordheim-plot. Linear regression then was applied to the data to extract the values of relative permittivity from Schottky-plot slope and Poole-Frenkel-plot slope. From Fowler-Nordheim-plot slope, the Fowler-Nordheim-energy-barrier was extracted. Example measurements in addition to a statistical overview of the results of all investigated samples are provided. Linear regression was applied to the region of the data that matches the realistic values most. It is concluded that ohmic injection and therefore SCLC only occurs at thicknesses below 12 nm and that the Poole-Frenkel-effect is no significant current limiting process. The extracted Fowler-Nordheim-barriers vary in the range of up to approximately 4 eV but do not show a specific trend. It is discussed whether the negative slope in the Fowler-Nordheim-plot could in some cases be a misinterpreted trap filled limit in the case of space charge limited current.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2014

Data mining for materials design: A computational study of single molecule magnet

We develop a method that combines data mining and first principles calculation to guide the designing of distorted cubane Mn{sup 4+} Mn {sub 3}{sup 3+} single molecule magnets. The essential idea of the method is a process consisting of sparse regressions and cross-validation for analyzing calculated data of the materials. The method allows us to demonstrate that the exchange coupling between Mn{sup 4+} and Mn{sup 3+} ions can be predicted from the electronegativities of constituent ligands and the structural features of the molecule by a linear regression model with high accuracy. The relations between the structural features and magnetic properties of the materials are quantitatively and consistently evaluated and presented by a graph. We also discuss the properties of the materials and guide the material design basing on the obtained results.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Three-dimensional flow contrast imaging of deep tissue using noncontact diffuse correlation tomography

This study extended our recently developed noncontact diffuse correlation spectroscopy flowmetry system into noncontact diffuse correlation tomography (ncDCT) for three-dimensional (3-D) flow imaging of deep tissue. A linear array of 15 photodetectors and two laser sources connected to a mobile lens-focusing system enabled automatic and noncontact scanning of flow in a region of interest. These boundary measurements were combined with a finite element framework for DCT image reconstruction implemented into an existing software package. This technique was tested in computer simulations and using a tissue-like phantom with anomaly flow contrast design. The cylindrical tube-shaped anomaly was clearly reconstructed in both simulation and phantom. Recovered and assigned flow contrast changes in anomaly were found to be highly correlated: regression slope = 1.00, R{sup 2} = 1.00, and p < 10{sup −5} in simulation and regression slope ≥ 0.97, R{sup 2} ≥ 0.96, and p < 10{sup −3} in phantom. These results exhibit promise of our ncDCT technique for 3-D imaging of deep tissue blood flow heterogeneities.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2014

Mathematical modelling for the drying method and smoothing drying rate using cubic spline for seaweed Kappaphycus Striatum variety Durian in a solar dryer

The solar drying experiment of seaweed using Green V-Roof Hybrid Solar Drier (GVRHSD) was conducted in Semporna, Sabah under the metrological condition in Malaysia. Drying of sample seaweed in GVRHSD reduced the moisture content from about 93.4% to 8.2% in 4 days at average solar radiation of about 600W/m{sup 2} and mass flow rate about 0.5 kg/s. Generally the plots of drying rate need more smoothing compared moisture content data. Special cares is needed at low drying rates and moisture contents. It is shown the cubic spline (CS) have been found to be effective for moisture-time curves. The idea of this method consists of an approximation of data by a CS regression having first and second derivatives. The analytical differentiation of the spline regression permits the determination of instantaneous rate. The method of minimization of the functional of average risk was used successfully to solve the problem. This method permits to obtain the instantaneous rate to be obtained directly from the experimental data. The drying kinetics was fitted with six published exponential thin layer drying models. The models were fitted using the coefficient of determination (R{sup 2}), and root mean square error (RMSE). The modeling of models using raw data tested with the possible of exponential drying method. The result showed that the model from Two Term was found to be the best models describe the drying behavior. Besides that, the drying rate smoothed using CS shows to be effective method for moisture-time curves good estimators as well as for the missing moisture content data of seaweed Kappaphycus Striatum Variety Durian in Solar Dryer under the condition tested.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2014

Bayesian data fusion for spatial prediction of categorical variables in environmental sciences

First developed to predict continuous variables, Bayesian Maximum Entropy (BME) has become a complete framework in the context of space-time prediction since it has been extended to predict categorical variables and mixed random fields. This method proposes solutions to combine several sources of data whatever the nature of the information. However, the various attempts that were made for adapting the BME methodology to categorical variables and mixed random fields faced some limitations, as a high computational burden. The main objective of this paper is to overcome this limitation by generalizing the Bayesian Data Fusion (BDF) theoretical framework to categorical variables, which is somehow a simplification of the BME method through the convenient conditional independence hypothesis. The BDF methodology for categorical variables is first described and then applied to a practical case study: the estimation of soil drainage classes using a soil map and point observations in the sandy area of Flanders around the city of Mechelen (Belgium). The BDF approach is compared to BME along with more classical approaches, as Indicator CoKringing (ICK) and logistic regression. Estimators are compared using various indicators, namely the Percentage of Correctly Classified locations (PCC) and the Average Highest Probability (AHP). Although BDF methodology for categorical variables is somehow a simplification of BME approach, both methods lead to similar results and have strong advantages compared to ICK and logistic regression.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Characterization of a 6 kW high-flux solar simulator with an array of xenon arc lamps capable of concentrations of nearly 5000 suns

A systematic methodology for characterizing a novel and newly fabricated high-flux solar simulator is presented. The high-flux solar simulator consists of seven xenon short-arc lamps mounted in truncated ellipsoidal reflectors. Characterization of spatial radiative heat flux distribution was performed using calorimetric measurements of heat flow coupled with CCD camera imaging of a Lambertian target mounted in the focal plane. The calorimetric measurements and images of the Lambertian target were obtained in two separate runs under identical conditions. Detailed modeling in the high-flux solar simulator was accomplished using Monte Carlo ray tracing to capture radiative heat transport. A least-squares regression model was used on the Monte Carlo radiative heat transfer analysis with the experimental data to account for manufacturing defects. The Monte Carlo ray tracing was calibrated by regressing modeled radiative heat flux as a function of specular error and electric power to radiation conversion onto measured radiative heat flux from experimental results. Specular error and electric power to radiation conversion efficiency were 5.92 ± 0.05 mrad and 0.537 ± 0.004, respectively. An average radiative heat flux with 95% errors bounds of 4880 ± 223 kW ⋅ m{sup −2} was measured over a 40 mm diameter with a cavity-type calorimeter with an apparent absorptivity of 0.994. The Monte Carlo ray-tracing resulted in an average radiative heat flux of 893.3 kW ⋅ m{sup −2} for a single lamp, comparable to the measured radiative heat fluxes with 95% error bounds of 892.5 ± 105.3 kW ⋅ m{sup −2} from calorimetry.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2015

A novel approach to correct the coded aperture misalignment for fast neutron imaging

Aperture alignment is crucial for the diagnosis of neutron imaging because it has significant impact on the coding imaging and the understanding of the neutron source. In our previous studies on the neutron imaging system with coded aperture for large field of view, “residual watermark,” certain extra information that overlies reconstructed image and has nothing to do with the source is discovered if the peak normalization is employed in genetic algorithms (GA) to reconstruct the source image. Some studies on basic properties of residual watermark indicate that the residual watermark can characterize coded aperture and can thus be used to determine the location of coded aperture relative to the system axis. In this paper, we have further analyzed the essential conditions for the existence of residual watermark and the requirements of the reconstruction algorithm for the emergence of residual watermark. A gamma coded imaging experiment has been performed to verify the existence of residual watermark. Based on the residual watermark, a correction method for the aperture misalignment has been studied. A multiple linear regression model of the position of coded aperture axis, the position of residual watermark center, and the gray barycenter of neutron source with twenty training samples has been set up. Using the regression model and verification samples, we have found the position of the coded aperture axis relative to the system axis with an accuracy of approximately 20 μm. Conclusively, a novel approach has been established to correct the coded aperture misalignment for fast neutron coded imaging.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

Design of low surface roughness-low residual stress-high optoelectronic merit a-IZO thin films for flexible OLEDs

The development of efficient and reliable large-area flexible optoelectronic devices demands low surface roughness-low residual stress-high optoelectronic merit transparent conducting oxide (TCO) thin films. Here, we correlate surface roughness-residual stress-optoelectronic properties of sputtered amorphous indium zinc oxide (a-IZO) thin films using a statistical design of experiment (DOE) approach and find a common growth space to achieve a smooth surface in a stress-free and high optoelectronic merit a-IZO thin film. The sputtering power, growth pressure, oxygen partial pressure, and RF/(RF+DC) are varied in a two-level system with a full factorial design, and results are used to deconvolve the complex growth space, identifying significant control growth parameters and their possible interactions. The surface roughness of a-IZO thin film varies over 0.19 nm to 3.97 nm, which is not in line with the general assumption of low surface roughness in a-IZO thin films. The initial regression model and analysis of variance reveal no single optimum growth sub-space to achieve low surface roughness (≤0.5 nm), low residual stress (−1 to 0 GPa), and industrially acceptable electrical conductivity (>1000 S/cm) for a-IZO thin films. The extrapolation of growth parameters in light of the current results and previous knowledge leads to a new sub-space, resulting in a low residual stress of −0.52±0.04 GPa, a low surface roughness of 0.55±0.03 nm, and moderate electrical conductivity of 1962±3.84 S/cm in a-IZO thin films. These results demonstrate the utility of the DOE approach to multi-parameter optimization, which provides an important tool for the development of flexible TCOs for the next-generation flexible organic light emitting diodes applications.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Hybrid PSO-ASVR-based method for data fitting in the calibration of infrared radiometer

The present paper describes a hybrid particle swarm optimization-adaptive support vector regression (PSO-ASVR)-based method for data fitting in the calibration of infrared radiometer. The proposed hybrid PSO-ASVR-based method is based on PSO in combination with Adaptive Processing and Support Vector Regression (SVR). The optimization technique involves setting parameters in the ASVR fitting procedure, which significantly improves the fitting accuracy. However, its use in the calibration of infrared radiometer has not yet been widely explored. Bearing this in mind, the PSO-ASVR-based method, which is based on the statistical learning theory, is successfully used here to get the relationship between the radiation of a standard source and the response of an infrared radiometer. Main advantages of this method are the flexible adjustment mechanism in data processing and the optimization mechanism in a kernel parameter setting of SVR. Numerical examples and applications to the calibration of infrared radiometer are performed to verify the performance of PSO-ASVR-based method compared to conventional data fitting methods.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2016

Concurrent thermochemoradiotherapy for brain high-grade glioma

Despite the achievements in the current strategies for treatment, the prognosis in malignant glioma patients remains unsatisfactory. Hyperthermia is currently considered to be the most effective and universal modifier of radiotherapy and chemotherapy. Preliminary treatment outcomes for 28 patients with newly diagnosed (23) and recurrent (5) high-grade gliomas were presented. All the patients received multimodality treatment including surgery, thermoche-moradiotherapy followed by 4 cycles of adjuvant chemotherapy. All the patients endured thermochemoradiotherapy well. A complication, limited skin burn (II stage), was diagnosed in two cases and treated conservatively without treatment interruption. A month after thermochemoradiotherapy the results were as follows: complete regression was achieved in 4 cases, partial regression in 4 cases, stable disease in 14 cases and disease progression in 6 cases (one of them is pseudo-progression). After completing the adjuvant chemotherapy 2 more patients demonstrated complete response and 1 patient had disease progression. Introduction of local hyperthermia in multimodal therapy of malignant glioma does not impair the combined modality treatment tolerability of patients with malignant gliomas. A small number of studied patients and short follow-up time do not allow making reliable conclusions about the impact of local hyperthermia on the treatment outcomes; however, there is a tendency towards the increase in disease-free survival in the patients with newly diagnosed malignant gliomas.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2016

The moisture outgassing kinetics of a silica reinforced polydimethylsiloxane

We investigated a silica-filled polydimethylsiloxane (PDMS) composite M9787 for potential outgassing in a vacuum/dry environment with the temperature programmed desorption/reaction method. The outgassing kinetics of 463 K vacuum heat-treated samples, vacuum heat-treated samples which were subsequently re-exposed to moisture, and untreated samples were extracted using the isoconversional and constrained iterative regression methods in a complementary fashion. Density functional theory (DFT) calculations of water interactions with a silica surface were also performed to provide insight into the structural motifs leading to the obtained kinetic parameters. Kinetic analysis/model revealed that no outgassing occurs from the vacuum heat-treated samples in subsequent vacuum/dry environment applications at room temperature (~300 K). Moreover, the main effect of re-exposure of the vacuum heat-treated samples to a glove box condition (~30 ppm by volume of H 2 O) for even a couple of days was the formation, on the silica surface fillers, of ~60 ppm by weight of physisorbed and loosely bonded moisture, which subsequently outgasses at room temperature in a vacuum/dry environment in a time span of 10 yr. However, without any vacuum heat treatment and even after 1 h of vacuum pump down, about 300 ppm by weight of H 2 O would be released from the PDMS in the next few hours. Thereafter the outgassing rate slows down substantially. Our presented methodology of using the isoconversional kinetic analysis results and some appropriate nature of the reaction as the constraints for more accurate iterative regression analysis/deconvolution of complex kinetic spectra, and of checking the so-obtained results with first principle calculations such as DFT can serve as a template for treating other complex physical/chemical processes as well.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Reference Correlation for the Viscosity of Carbon Dioxide

A comprehensive database of experimental and computed data for the viscosity of carbon dioxide (CO 2 ) was compiled and a new reference correlation was developed. Literature results based on an ab initio potential energy surface were the foundation of the correlation of the viscosity in the limit of zero density in the temperature range from 100 K to 2000 K. Guided symbolic regression was employed to obtain a new functional form that extrapolates correctly to T → 0 K and to 10 000 K. Coordinated measurements at low density made it possible to implement the temperature dependence of the Rainwater-Friend theory in the linear-in-density viscosity term. The residual viscosity could be formulated with a scaling term ργ/T the significance of which was confirmed by symbolic regression. The final viscosity correlation covers temperatures from 100 K to 2000 K for gaseous CO 2 , and from 220 K to 700 K with pressures along the melting line up to 8000 MPa for compressed and supercritical liquid states. The data representation is more accurate than with the previous correlations, and the covered pressure and temperature range is significantly extended. The critical enhancement of the viscosity of CO 2 is included in the new correlation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Time reversal symmetry breaking and odd viscosity in active fluids: Green–Kubo and NEMD results

Active fluids, which are driven at the microscale by non-conservative forces, are known to exhibit novel transport phenomena due to the breaking of time reversal symmetry. Recently, Epstein and Mandadapu [arXiv:1907.10041 (2019)] obtained Green-Kubo relations for the full set of viscous coefficients governing isotropic chiral active fluids, including the so-called odd viscosity, invoking Onsager's regression hypothesis for the decay of fluctuations in active non-equilibrium steady states. In this Communication, we test these Green-Kubo relations using molecular dynamics simulations of a canonical model system consisting of actively torqued dumbbells. We find the resulting odd and shear viscosity values from the Green-Kubo relations to be in good agreement with values measured independently through non-equilibrium molecular dynamics flow simulations. This provides a test of the Green-Kubo relations and lends support to the application of the Onsager regression hypothesis in relation to viscous behaviors of active matter systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Operator learning for predicting multiscale bubble growth dynamics

We report simulating and predicting multiscale problems that couple multiple physics and dynamics across many orders of spatiotemporal scales is a great challenge that has not been investigated systematically by deep neural networks (DNNs). Herein, we develop a framework based on operator regression, the so-called deep operator network (DeepONet), with the long-term objective to simplify multiscale modeling by avoiding the fragile and time-consuming “hand-shaking” interface algorithms for stitching together heterogeneous descriptions of multiscale phenomena. To this end, as a first step, we investigate if a DeepONet can learn the dynamics of different scale regimes, one at the deterministic macroscale and the other at the stochastic microscale regime with inherent thermal fluctuations. Specifically, we test the effectiveness and accuracy of the DeepONet in predicting multirate bubble growth dynamics, which is described by a Rayleigh–Plesset (R–P) equation at the macroscale and modeled as a stochastic nucleation and cavitation process at the microscale by dissipative particle dynamics (DPD). First, we generate data using the R–P equation for multirate bubble growth dynamics caused by randomly time-varying liquid pressures drawn from Gaussian random fields (GRFs). Our results show that properly trained DeepONets can accurately predict the macroscale bubble growth dynamics and can outperform long short-term memory networks. We also demonstrate that the DeepONet can extrapolate accurately outside the input distribution using only very few new measurements. Subsequently, we train the DeepONet with DPD data corresponding to stochastic bubble growth dynamics. Although the DPD data are noisy and we only collect sparse data points on the trajectories, the trained DeepONet model is able to predict accurately the mean bubble dynamics for time-varying GRF pressures. Taken together, our findings demonstrate that DeepONets can be employed to unify the macroscale and microscale models of the multirate bubble growth problem, hence providing new insight into the role of operator regression via DNNs in tackling realistic multiscale problems and in simplifying modeling with heterogeneous descriptions.

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