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DOE OSTI2023

Rapid discovery of high hardness multi-principal-element alloys using a generative adversarial network model

Multi-principal element alloys (MPEAs) continue to gain research prominence due to their promising high-temperature microstructural and mechanical properties. Recently, machine learning (ML) and materials informatics have been used extensively for screening MPEAs, however, most of these efforts were focused on constructing classification and regression models for predicting phase stability and mechanical properties of known compositions. These approaches may accelerate the screening process but optimizing new compositions with desirable properties within a practical time frame from an infinitely large design space of MPEA systems remains a grand challenge. To tackle this composition optimization challenge, a generative adversarial network coupled with a neural-network ML model was utilized to design MPEAs by filtering compositions that have high hardness. Even in a high-dimensional space with 18 elements as descriptors, the ML model was able to generate optimized compositions from which one composition was found to have 10% higher hardness (941 HV) than the maximum in the training data (857 HV). Density-functional theory was used to provide thermodynamic and electronic insights to higher hardness of the new MPEA found. The present work can optimize compositions from a wide design space of 18 elements (including W, Ta and Nb) that presents an opportunity to synthesize new compositions for applications ranging from corrosion-resistant alloys to nuclear materials. Here the findings suggest that generative ML can greatly accelerate materials discovery by identifying novel compositions, which can serve as a data-informed tool to guide experiments.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Pre-vaporized ignition behavior of ethyl- and propyl-terminated oxymethylene ethers

Oxymethylene ethers (OMEs) have been studied in recent years for use as compression ignition fuel blendstocks, but the methyl-terminated OMEs commonly studied exhibit properties that are poorly optimized for engine use and distribution. Recent work has shown that OMEs with larger (ethyl, propyl, or butyl) end groups may have superior properties for fuel usage/storage. In this work, we consider ignition of four OMEs - diethoxymethane (E-1-E), dipropoxymethane (P-1-P), ethoxy-(methoxy) 2 -ethane (E-2-E), and diisopropoxymethane (iP-1-iP) - as representatives of the possible effects of changes to OME structures. To our knowledge, ignition behaviors of the latter three fuels have not been studied prior to this work. Further, we find that all of the tested linear OMEs (E-1-E, P-1-P, and E-2-E) show two-stage ignition at low temperatures and nonlinear ignition behavior, consistent with literature on methyl-terminated OMEs and E-1-E. The nonlinear, branched OME (iP-1-iP) required higher pressure and temperature to ignite than the linear OMEs; further, this fuel experienced only single stage ignition and a linear ignition delay curve. By analogy to existing kinetic mechanisms for ethers and higher alcohols, the chemical basis for the observed trends are hypothesized. Faster ignition of E-2-E results from the additional oxymethylene group providing additional sites for ROO formation and more possible QOOH structures. Slower low temperature ignition of P-1-P is driven by lower H abstraction rates in comparison to E-1-E, however at high temperatures P-1-P ignites faster, driven by increasing abstraction from the additional H site on the propyl group that opens up additional QOOH formation pathways. iP-1-iP ignition is slowed significantly by preferential H abstraction from the central carbon of the isopropyl group, which is crowded and unlikely to bond with O 2 , however at high temperatures, abstraction from H sites on the methyl groups allows for the ROO cascade initiation and subsequent rapid ignition.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Evaluation of DED and LPBF Fe-based Alloys Process Application Envelopes based on Performance, Process Economics, Supply Chain Risks, and Reactor-specific Targeted Components

The U.S. Department of Energy (DOE), Office of Nuclear Energy (NE), Advanced Materials and Manufacturing Technologies (AMMT) program aims to develop extreme-environment materials solutions for use in the deployment of advanced nuclear reactors and the sustainment of the current fleet. To achieve this objective, a combination of experiment, a computational tool, and machine learning (ML) for the design of materials is adopted for the maturation of materials for nuclear technology. Through advanced manufacturing techniques such as laser powder bed fusion (LPBF) and laser powder direct energy deposition (LP-DED), components with complex geometries can be fabricated with reduced time and effort. Such advanced manufacturing methods can also provide the opportunity to improve materials performance through optimized microstructures and mechanical properties. However, existing engineering alloys are not always well suited for fabrication with additive manufacturing (AM), as their compositions have been tuned to optimize fabrication via conventional methods. Thus, similar alloys with modified compositions that are better suited for AM can be studied for improved performance. Over the past three years, the AMMT teams from Argonne National Laboratory (ANL) and Pacific Northwest National Laboratory (PNNL) studied various known Fe-based alloys by evaluating their initial printability using LPBF, and an AMMT-developed down-selection and decision matrix reduced the number of alloys to be studied from six to three in fiscal year (FY) 2024. Additionally, in FY 2024, for parallel evaluation, these three alloys were studied using LPDED. While LPBF is better for small- to medium-sized components with high detail and internal features, LP-DED combines a material feed system to place the powder onto the exact spot where the laser will melt the material. This AM method can be easily scaled to extremely large components and provides high build rate speeds compared to those of conventional LPBF systems. Additionally, DED is a better choice for complex geometries and compositional gradients.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2025

Measurements of source emittance and beam coherence properties of the upgraded Advanced Photon Source

The Advanced Photon Source (APS) has been upgraded with a multi-bend achromat lattice, achieving significantly reduced electron beam emittance and enhanced X-ray coherence. Precise characterization of these properties is essential for optimizing beamline performance and enabling new experiments that take full advantage of the upgraded source. We report on measurements of source size and transverse coherence properties using grating interferometry at two beamlines: the APS 3-ID-B undulator beamline and the 1-BM-B bending magnet beamline. The results confirm the world-record horizontal emittance of the upgraded APS below 30 pm rad and validate the theoretical design parameters. We further investigate the impact of optical aberrations and mechanical vibrations at the 12-ID-C beamline on coherence preservation. These measurements establish a benchmark for future beamline enhancements and demonstrate the effectiveness of grating interferometry for high-precision beam characterization. Our findings provide critical insights into synchrotron beam dynamics, coherence degradation mechanisms, and strategies for optimizing beamline X-ray optics at next-generation light sources.

Advanced Photon Source↗

DOE OSTI2017

Antifogging abilities of model nanotextures

Nanometre-scale features with special shapes impart a broad spectrum of unique properties to the surface of insects. These properties are essential for the animal’s survival, and include the low light reflectance of moth eyes, the oil repellency of springtail carapaces and the ultra-adhesive nature of palmtree bugs. Antireflective mosquito eyes and cicada wings are also known to exhibit some antifogging and self-cleaning properties. In all cases, the combination of small feature size and optimal shape provides exceptional surface properties. In this work, we investigate the underlying antifogging mechanism in model materials designed to mimic natural systems, and explain the importance of the texture’s feature size and shape. While exposure to fog strongly compromises the water-repellency of hydrophobic structures, this failure can be minimized by scaling the texture down to nanosize. Furthermore, this undesired effect even becomes non-measurable if the hydrophobic surface consists of nanocones, which generate antifogging efficiency close to unity and water departure of droplets smaller than 2 μm.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2025

Uncovering multiscale structure-property correlations via active learning in scanning tunneling microscopy

Atomic arrangements and local sub-structures fundamentally influence emergent material functionalities. These structures are conventionally probed using spatially resolved studies and the property correlations are deciphered by a researcher based on sequential explorations, thereby limiting the efficiency and scope. Here we demonstrate a multi-scale Bayesian deep-learning based framework that automatically correlates material structure with its electronic properties using scanning tunneling microscopy (STM) measurements in real-time. Its predictions are used to autonomously direct exploration toward regions of the sample that optimize a given material property. This method is deployed on a low-temperature ultra-high vacuum STM to understand the structure-property relationship in a europium-based semimetal, EuZn 2 As 2 , a promising candidate relevant to magnetism-driven topological phenomena. The framework employs a sparse-sampling approach to efficiently construct the scalar-property space using minimal measurements, about 1–10% of the data required in standard hyperspectral methods. Moreover, we formulate the problem hierarchically across length scales, implementing autonomous workflow to locate mesoscopic and atomic structures that correspond to a target material property. This framework offers the choice to design scalar-property from the spectroscopic data to steer sample exploration. Our findings reveal correlations of the electronic properties unique to surface terminations, local defect density, and point defects.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2025

Enabling Partnership between South Carolina and NREL for Advancing Opportunities in Plastics Recycling Research (EPSCOR for Plastics Recycling)

Proposal Objectives: 1. Utilize depolymerization/fractionation techniques to recover highly processable and reactive feedstocks for polymer synthesis from lignin. 2. Synthesize lignin‐derived non‐isocyanate polyurethane, epoxy, and polyamide using non‐ toxic, biobased route designed for chemical recycling. Characterize resulting materials. 3. Design a high‐yielding chemical recycling process for as‐synthesized materials yielding usable building blocks for many generations of polymer synthesis. 4. Optimize chemical recycling of PET waste for the synthesis of lignin‐based polymers. Compare properties to commercial materials. 5. Optimize reaction conditions and recycling steps to facilitate enhanced sustainability of the synthetic steps and final properties of materials. 6. Complete a lifecycle assessment of lignin utilization and chemical recycling to compare their environmental performance to that of materials produced from virgin material. Identify hot spots and benefits using the chemical recycling process.

36 MATERIALS SCIENCE↗

DOE OSTI2016

High thermoelectric potential of Bi{sub 2}Te{sub 3} alloyed GeTe-rich phases

In an attempt to reduce our reliance on fossil fuels, associated with severe environmental effects, the current research is focused on the identification of the thermoelectric potential of p-type (GeTe){sub 1−x}(Bi{sub 2}Te{sub 3}){sub x} alloys, with x values of up to 20%. Higher solubility limit of Bi{sub 2}Te{sub 3} in GeTe, than previously reported, was identified around ∼9%, extending the doping potential of GeTe by the Bi{sub 2}Te{sub 3} donor dopant, for an effective compensation of the high inherent hole concentration of GeTe toward thermoelectrically optimal values. Around the solubility limit of 9%, an electronic optimization resulted in an impressive maximal thermoelectric figure of merit, ZT, of ∼1.55 at ∼410 °C, which is one of the highest ever reported for any p-type GeTe-rich alloys. Beyond the solubility limit, a Fermi Level Pinning effect of stabilizing the Seebeck coefficient was observed in the x = 12%–17% range, leading to stabilization of the maximal ZTs over an extended temperature range; an effect that was associated with the potential of the governed highly symmetric Ge{sub 8}Bi{sub 2}Te{sub 11} and Ge{sub 4}Bi{sub 2}Te{sub 7} phases to create high valence band degeneracy with several bands and multiple hole pockets on the Fermi surface. At this compositional range, co-doping with additional dopants, creating shallow impurity levels (in contrast to the deep lying level created by Bi{sub 2}Te{sub 3}), was suggested for further electronic optimization of the thermoelectric properties.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2021

Flexible optimized data handling in systems with multiple memories

Methods and systems for optimizing an application for a computing system having multiple distinct memory locations that are interconnected by one or more communication channels include determining one or more data handling properties for a data region in an application. One or more data handling policies for the data region are determined based on the one or more data handling properties. Data setup costs are determined for a scope in the application that uses the data region in different memory locations based on the one or more data handling properties. The application is optimized in accordance with the one or more data handling policies and the data setup costs for the different memory locations.

Chen, Tong↗

DOE OSTI2023

Flexible optimized data handling in systems with multiple memories

Chen, Tong↗

DOE OSTI2015

Multilayer, Multimaterial Thermal Barrier Coating Systems: Design, Synthesis, and Performance Assessment

Thermal barrier coatings ( TBC s) are increasingly playing a vital role in enhancing efficiency and performance of gas turbine engines. As engine operating temperatures rise, yttria‐stabilized zirconia ( YSZ ), the currently principal TBC material, reaches its operational limits. Gadolinium Zirconate ( GDZ )‐based pyrochlore oxides are now emerging contenders, not only due to their lower thermal conductivity, but also their ability to resist attack by silicate deposits. However, GDZ cannot be directly substituted for YSZ due to its incompatibility with the thermally grown alumina layer, therefore requiring to be a component of multilayer system. Although industry has already adopted these materials in various applications, a number of fundamental issues arise with respect to layered‐coating design, their properties and processing dependence. In this study several multilayer architectures, based on the YSZ – GDZ system, have been developed and tested for durability under furnace thermal cycling conditions. Coating designs considered optimization of microstructure and properties of individual layers based on their location within the top‐coat thickness to address competing interests of thermal conductivity, compliance, and resistance to silicates. A large variance in durability was observed in coatings made with different multilayer designs. The results and the associated failure mechanisms are rationalized through preliminary evaluation of elastic energies at the failure locations and corresponding energy release rates. The results point to new strategies in the design and manufacturing of optimal multilayer coatings.

Viswanathan, Vaishak↗

DOE OSTI2013

Optimization Using Metamodeling in the Context of Integrated Computational Materials Engineering (ICME)

Predictive Design Technologies, LLC (PDT) proposed to employ Integrated Computational Materials Engineering (ICME) tools to help the manufacturing industry in the United States regain the competitive advantage in the global economy. ICME uses computational materials science tools within a holistic system in order to accelerate materials development, improve design optimization, and unify design and manufacturing. With the advent of accurate modeling and simulation along with significant increases in high performance computing (HPC) power, virtual design and manufacturing using ICME tools provide the means to reduce product development time and cost by alleviating costly trial-and-error physical design iterations while improving overall quality and manufacturing efficiency. To reduce the computational cost necessary for the large-scale HPC simulations and to make the methodology accessible for small and medium-sized manufacturers (SMMs), metamodels are employed. Metamodels are approximate models (functional relationships between input and output variables) that can reduce the simulation times by one to two orders of magnitude. In Phase I, PDT, partnered with Mississippi State University (MSU), demonstrated the feasibility of the proposed methodology by employing MSU?s internal state variable (ISV) plasticity-damage model with the help of metamodels to optimize the microstructure-process-property-cost for tube manufacturing processes used by Plymouth Tube Company (PTC), which involves complicated temperature and mechanical loading histories. PDT quantified the microstructure-property relationships for PTC?s SAE J525 electric resistance-welded cold drawn low carbon hydraulic 1010 steel tube manufacturing processes at seven different material states and calibrated the ISV plasticity material parameters to fit experimental tensile stress-strain curves. PDT successfully performed large scale finite element (FE) simulations in an HPC environment using the ISV plasticity model in Abaqus FE analyses of the tube forming, sizing, drawing, welding, and normalizing processes. The simulation results coupled with the manufacturing cost data were used to develop prototype metamodeling (quick response) codes which could be used to predict and optimize the microstructure-process-property-cost relationships. The developed ICME metamodeling toolkits are flexible enough to be applied to other manufacturing processes (e.g. forging, forming, casting, extrusion, rolling, stamping, and welding/joining) and metamodeling codes can run on laptop computers. Based on the work completed in Phase I, in Phase II, PDT proposes to continue to refine the ISV model by correlating and incorporating the uncertainties in the microstructure, mechanical testing, and modeling. Following the model refinement, FE analyses will be simulated and will provide even more realistic predictions as they include an appropriate window of uncertainty. Using the HPC output (FE analyses) as input, the quick-response metamodel codes will more accurately predict and optimize the microstructure-process-property-cost relationships. Furthermore, PDT propose to employ the ICME metamodeling toolkits to help develop a new tube product using entirely new high strength steel. The modeling of the high strength steel manufacturing process will replace the costly and time consuming trial-and-error methods that were used in the tubing industry previously. This simulation-based process prototyping will greatly benefit our industrial partners by opening up new market spaces due to new products with greater capabilities.

36 MATERIALS SCIENCE↗

DOE OSTI2016

The CU 2-D-MAX-DOAS instrument – Part 2: Raman scattering probability measurements and retrieval of aerosol optical properties

Abstract. The multiannual global mean of aerosol optical depth at 550 nm (AOD550) over land is ∼ 0.19, and that over oceans is ∼ 0.13. About 45 % of the Earth surface shows AOD550 smaller than 0.1. There is a need for measurement techniques that are optimized to measure aerosol optical properties under low AOD conditions. We present an inherently calibrated retrieval (i.e., no need for radiance calibration) to simultaneously measure AOD and the aerosol phase function parameter, g, based on measurements of azimuth distributions of the Raman scattering probability (RSP), the near-absolute rotational Raman scattering (RRS) intensity. We employ radiative transfer model simulations to show that for solar azimuth RSP measurements at solar elevation and solar zenith angle (SZA) smaller than 80°, RSP is insensitive to the vertical distribution of aerosols and maximally sensitive to changes in AOD and g under near-molecular scattering conditions. The University of Colorado two-dimensional Multi-AXis Differential Optical Absorption Spectroscopy (CU 2-D-MAX-DOAS) instrument was deployed as part of the Two Column Aerosol Project (TCAP) at Cape Cod, MA, during the summer of 2012 to measure direct sun spectra and RSP from scattered light spectra at solar relative azimuth angles (SRAAs) between 5 and 170°. During two case study days with (1) high aerosol load (17 July, 0.3 < AOD430 < 0.6) and (2) near-molecular scattering conditions (22 July, AOD430 < 0.13) we compare RSP-based retrievals of AOD430 and g with data from a co-located CIMEL sun photometer, Multi-Filter Rotating Shadowband Radiometer (MFRSR), and an airborne High Spectral Resolution Lidar (HSRL-2). The average difference (relative to DOAS) for AOD430 is +0.012 ± 0.023 (CIMEL), −0.012 ± 0.024 (MFRSR), −0.011 ± 0.014 (HSRL-2), and +0.023 ± 0.013 (CIMELAOD − MFRSRAOD) and yields the following expressions for correlations between different instruments: DOASAOD = −(0.019 ± 0.006) + (1.03 ± 0.02) × CIMELAOD (R2 = 0.98), DOASAOD = −(0.006 ± 0.005) + (1.08 ± 0.02) × MFRSRAOD (R2 = 0.98), and CIMELAOD = (0.013 ± 0.004) + (1.05 ± 0.01) × MFRSRAOD (R2 = 0.99). The average g measured by DOAS on both days was 0.66 ± 0.03, with a difference of 0.014 ± 0.05 compared to CIMEL. Active steps to minimize the error in the RSP help to reduce the uncertainty in retrievals of AOD and g. As AOD decreases and SZA increases, the RSP signal-to-noise ratio increases. At AOD430 ∼ 0.4 and 0.10 the absolute AOD errors are ∼ 0.014 and 0.003 at 70° SZA and 0.02 and 0.004 at 35° SZA. Inherently calibrated, precise AOD and g measurements are useful to better characterize the aerosol direct effect in urban polluted and remote pristine environments.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Machine-learning-accelerated multimodal characterization and multiobjective design optimization of natural porous materials

Natural porous materials such as nanoporous clays are used as green and low-cost adsorbents and catalysts. The key factors determining their performance in these applications are the pore morphology and surface activity, which are typically represented by properties such as specific surface area, pore volume, micropore content and pH. The latter may be modified and tuned to specific applications through material processing and/or chemical treatment. Characterization of the material, raw or processed, is typically performed experimentally, which can become costly especially in the context of tuning of the properties towards specific application requirements and needing numerous experiments. In this work, we present an application of tree-based machine learning methods trained on experimental datasets to accelerate the characterization of natural porous materials. The resulting models allow reliable prediction of the outcomes of experimental characterization of processed materials (R2 from 0.78 to 0.99) as well as identification of key factors contributing to those properties through feature importance analysis. Furthermore, the high throughput of the models enables exploration of processing parameter–property correlations and multiobjective optimization of prototype materials towards specific applications. We have applied these methodologies to pinpoint and rationalize optimal processing conditions for clays exploitable in acid catalysis. One of such identified materials was synthesized and tested revealing appreciable acid character improvement with respect to the pristine material. Specifically, it achieved 79% removal of chlorophyll-a in acid catalyzed degradation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2018

Active learning for accelerated design of layered materials

Hetero-structures made from vertically stacked monolayers of transition metal dichalcogenides hold great potential for optoelectronic and thermoelectric devices. Discovery of the optimal layered material for specific applications necessitates the estimation of key material properties, such as electronic band structure and thermal transport coefficients. However, screening of material properties via brute force ab initio calculations of the entire material structure space exceeds the limits of current computing resources. Moreover, the functional dependence of material properties on the structures is often complicated, making simplistic statistical procedures for prediction difficult to employ without large amounts of data collection. Here, we present a Gaussian process regression model, which predicts material properties of an input hetero-structure, as well as an active learning model based on Bayesian optimization, which can efficiently discover the optimal hetero-structure using a minimal number of ab initio calculations. The electronic band gap, conduction/valence band dispersions, and thermoelectric performance are used as representative material properties for prediction and optimization. The Materials Project platform is used for electronic structure computation, while the BoltzTraP code is used to compute thermoelectric properties. Bayesian optimization is shown to significantly reduce the computational cost of discovering the optimal structure when compared with finding an optimal structure by building a regression model to predict material properties. The models can be used for predictions with respect to any material property and our software, including data preparation code based on the Python Materials Genomics (PyMatGen) library as well as python-based machine learning code, is available open source.

36 MATERIALS SCIENCE↗

DOE OSTI2012

Structural and electronic properties of Sr{sub x}Ba{sub 1-x}SnO{sub 3} from first principles calculations

Neutron diffraction data for Sr{sub x}Ba{sub 1-x}SnO{sub 3} (x=0.0, 0.2, 0.4, 0.6, 0.8 and 1.0) solid solutions were used as inputs to obtain optimized geometries and electronic properties using the density functional theory (DFT) formalism considering both the local density and generalized gradient approximations, LDA and GGA, respectively. The crystal structures and SnO{sub 6} octahedra tilting angles found after total energy minimization agree well with experiment, specially for the GGA data. Elastic constants were also obtained and compared with theoretical and experimental results for cubic BaSnO{sub 3}. While the alloys with cubic unit cell have an indirect band gap, tetragonal and orthorhombic alloys exhibit direct band gaps (exception made to x=1.0). The Kohn-Sham minimum electronic band gap oscillates from 1.52 eV (cubic x=0.0, LDA) to 2.61 eV (orthorhombic x=1.0, LDA), and from 0.74 eV (cubic BaSnO{sub 3}, GGA) to 1.97 eV (orthorhombic SrSnO{sub 3}, GGA). Parabolic interpolation of bands has allowed us to estimate the effective masses for charge carriers, which are shown to be anisotropic and larger for holes. - Graphical Abstract: Highlights: Black-Right-Pointing-Pointer DFT calculations were performed on Sr{sub x}Ba{sub 1-x}SnO{sub 3} solid solutions. Black-Right-Pointing-Pointer Calculated crystal structures agree well with experiment. Black-Right-Pointing-Pointer Alloys have direct or indirect gaps depending on the Sr molar fraction. Black-Right-Pointing-Pointer The Kohn-Sham gap variation from x=0.0 to x=1.0 is close to the experimental value. Black-Right-Pointing-Pointer Carrier effective masses are very anisotropic, specially for holes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2016

Simulated Gamma Doses to Coating Materials for Use in High Level Waste Packages

An outer shield barrier for spent nuclear fuel is being developed in order to improve dry cask performance under mechanical, chemical, and radiological degradation and stresses. The materials proposed for the outer shield barrier are titanium nitride (TiN), a composite oxide layer consisting of ZrO{sub 2}, TiO{sub 2}, and Al{sub 2}O{sub 3}, and a final layer of molybdenum disulfide (MoS{sub 2}). The ultimate aim of this research is to develop a multi component shield material either as multiple layers or a multi material composite that will improve the performance of steel used in long-term storage casks. Two approaches are being examined through this research effort: multilayer and composite. The composition of the oxide layer is crucial to optimizing the shield's properties and performance. At equal oxide compositions the mixture of these specific oxides is commonly known as zirconolite. This material has been shown to provide a water barrier and to provide substantial corrosion resistance to the dry cask. In order to optimize the radiological performance of this layer the compositions of each oxide have been varied and analyzed for chemical, mechanical, and radiological effects on performance. Through the course of this research effort materials performance changes after gamma and neutron exposure as well as gamma and neutron attenuation of the potential shield materials have been measured and correlated to coating composition. As it is particularly difficult to design a novel, five component material with the goal of improving corrosion, wear behavior, heat tolerance, radiation tolerance, and general survivability, it is necessary to understand which coating components contribute to each property enhancement, and which are critical to design and eventual coating survivability. To compliment the experimental research effort, a study of gamma radiation doses using the relevant zirconolite constituent oxides and assorted combinations off all three, ZrO{sub 2}-TiO{sub 2}-Al{sub 2}O{sub 3}, has been conducted in order to gain insight into which, if any, ratios of the three constituent oxides may provide optimal gamma radiation shielding. Analyzing and comparing the oxides to each other it is apparent that the outer shield barrier with no zirconia has the highest absorbed dose rate at 4.88 E6 mrad/hr. The simulations run with no alumina and no titania are remarkably similar in absorbed dose rates. Alumina and titania compositions increase the absorbed dose rate increase. This is to be expected as during these simulations the zirconia composition is decreasing.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2023

Integrated data-driven and experimental approaches to accelerate lead optimization targeting SARS-CoV- 2 main protease

Identification of potential therapeutic candidates can be expedited by integrating computational modeling with domain aware machine learning (ML) approaches followed by experimental validation. Generative deep learning models have been recently developed that can generate thousands of new candidates, but their physiochemical properties are typically not optimized. Using our deep learning models and a scaffold as a starting point, we generated tens of thousands of compounds for SARS-CoV-2 M pro that preserve the core scaffold. Here we utilized and implemented several computational tools such as structural alert and toxicity analysis, high throughput virtual screening, ML-based 3D quantitative structure–activity relationships, multi-parameter optimization, and graph neural networks on libraries of generated candidates to predict biological activity and binding affinity a priori. From these collective computational results, eight promising candidates were identified and tested experimentally using Native Mass Spectrometry (MS) and FRET-based functional assays. Two compounds, with quinazoline-2-thiol and acetylpiperidine core moiety showed IC 50 values in the low micromolar range: 2.95±0.0017 µM and 3.41±0.0015 µM, respectively. The molecular dynamics simulations further highlight that binding of these compounds results in allosteric modulations in the chain B and the interface domains of the M pro . The key fragments from these top hits can be used as input for closed loop lead optimization in the integrated pipeline.

60 APPLIED LIFE SCIENCES↗