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DOE OSTI2021

Insights from Computational Studies on the Anisotropic Volume Change of Li x NiO 2 at High States of Charge (x < 0.25)

The need for high-capacity Li-ion battery cathodes has favored the increase of Ni content in commercial battery cells. However, at high states of charge (SOCs), Ni-rich materials undergo a phase transition and volume collapse with deleterious effects on battery performance. It is uncertain whether this drastic volume change is caused by the phase transition or not. To provide more insight into the volume-phase transition relationship in the high Ni cathode LixNiO 2 , we performed density functional theory calculations, along with molecular dynamics simulations using machine learning potentials to calculate the temperature- and composition-dependent free energy differences between the suspected phases at high SOCs (x < 0.25). Herein, we find that the calculated free energy difference between the suspected phases containing different oxygen stacking sequences is small at room temperature. Furthermore, we find that the collapse of the layered LiNiO 2 c-lattice parameter at high SOCs is mainly due to the electronic depletion of the oxygen sublattice and the lack of screening from positive Li ions. The interactions between adjacent oxygen ions across an empty Li layer (NiO 2 ) are largely controlled by van der Waals interactions and are in fact similar regardless of the oxygen stacking, which explains the negligible free energy differences between O1 and O3 stacking in NiO 2 .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Developing a complete AI-accelerated workflow for superconductor discovery

The quest to identify new superconducting materials with enhanced properties is hindered by the prohibitive cost of computing electron-phonon spectral functions, severely limiting the materials space that can be explored. Here, we introduce a Bootstrapped Ensemble of Equivariant Graph Neural Networks (BEE-NET), a machine-learning model trained to predict the Eliashberg spectral function and superconducting critical temperature with a mean-absolute-error of 0.87 K relative to DFT-based Allen-Dynes calculations. Intriguingly, BEE-NET achieves a true-negative-rate of 99.4%, enabling highly efficient screening for the rare property of superconductivity. Integrated into a multi-stage, AI-accelerated discovery pipeline that incorporates elemental-substitution strategies and machine-learned interatomic potentials, our workflow reduced over 1.3 million candidate structures to 741 dynamically and thermodynamically stable compounds with DFT-confirmed T c > 5 K. We report the successful synthesis and experimental confirmation of superconductivity in two of these previously unreported compounds. This study establishes a data-driven framework that integrates machine learning, quantum calculations, and experiments to systematically accelerate superconductor discovery.

Gibson, Jason B. [Quantum Formatics, Cambridge, MA↗

DOE OSTI2021

First-principles study of electron transport in ScN

We investigate the conduction-band structure and electron mobility in rocksalt ScN based on density functional theory. The first-principles band structure allows us to obtain band velocities and effective masses as a function of energy. Electron-phonon scattering is assessed by explicitly computing the q-dependent electron-phonon matrix elements, with the inclusion of the long-range electrostatic interaction. The influence of free-carrier screening on the electron transport is assessed using the random phase approximation. We find a notable enhancement of electron mobility when the carrier concentration exceeds 10 20 cm -3 . We calculate the room-temperature electron mobility in ScN to be 587 cm2 /Vs at low carrier concentrations. When the carrier concentration is increased, the electron mobility starts to decrease significantly around n = 10 19 cm -3 , and drops to 240 cm 2 /Vs at n = 10 21 cm -3 . We also explore the influence of strain in (111)- and (100)-oriented ScN films. For (111) films, we find that a 1.0% compressive epitaxial strain increases the in-plane mobility by 72 cm 2 /Vs and the out-of-plane mobility by 50 cm 2 /Vs. For (100) films, a 1.0% compressive epitaxial strain increases the out-of-plane mobility by as much as 172 cm2 /Vs, but has a weak impact on the in-plane mobility. Our study sheds light on electron transport in ScN at different electron concentrations and shows how strain engineering could increase the electron mobility.

36 MATERIALS SCIENCE↗

NASA NTRS1972

Development of seal ring carbon-graphite materials (tasks 5, 6, and 7)

Carbon-graphite seal ring bodies for operation at air temperatures to 1300 F(704 C) were manufactured from three select formulations. Mechanical and thermal properties, porosities, and oxidation rates were measured. The results have shown that: (1) Major property improvements anticipated from the screening studies were not realized because of processing problems associated with the scale-up in material size and probable deterioration of a phenolic resin binder; (2) the mechanical properties of a phenolic resin-bonded, carbon-graphite material can be improved by applying high pressure during carbonization; and (3) the textile form of graphite fiber used as the minor filler component in a carbon-graphite material can beneficially affect mechanical properties.

Fechter, N. J.↗

NASA NTRS1978

Concentration-response data on toxicity of pyrolysis gases from six synthetic polymers

Concentration-response data are presented on the toxic effects of the pyrolysis gases from six synthetic polymers on Swiss Webster male mice, using a toxicity screening test method. The polymers consisted of one sample each of polycarbonate and polystyrene, and two samples each of polyethylene and acrylonitrile-butadiene-styrene (ABS). All six samples were evaluated using the rising temperature program, and three samples were evaluated using the fixed temperature program. The pyrolysis gases from polycarbonate, polyethylene, and ABS appeared to exhibit the concentration-response relationships commonly encountered in toxicology. The polystyrene sample gave variable results; probably due to poor reproducibility of the essentially anaerobic pyrolysis. Carbon monoxide seemed to be the principal toxicant in the pyrolysis gases from polycarbonate and polyethylene, but did not appear to be the principal toxicant in the pyrolysis gases from ABS.

Hilado, C. J.↗

NASA NTRS2011

Improvements to a Response Surface Thermal Model for Orion

A study was performed to determine if a Design of Experiments (DOE)/Response Surface Methodology could be applied to on-orbit thermal analysis and produce a set of Response Surface Equations (RSE) that predict Orion vehicle temperatures within 10 F. The study used the Orion Outer Mold Line model. Five separate factors were identified for study: yaw, pitch, roll, beta angle, and the environmental parameters. Twenty-three external Orion components were selected and their minimum and maximum temperatures captured over a period of two orbits. Thus, there are 46 responses. A DOE case matrix of 145 runs was developed. The data from these cases were analyzed to produce a fifth order RSE for each of the temperature responses. For the 145 cases in the DOE matrix, the agreement between the engineering data and the RSE predictions was encouraging with 40 of the 46 RSEs predicting temperatures within the goal band. However, the verification cases showed most responses did not meet the 10 F goal. After reframing the focus of the study to better align the RSE development with the purposes of the model, a set of RSEs for both the minimum and maximum radiator temperatures was produced which predicted the engineering model output within +/-4 F. Therefore, with the correct application of the DOE/RSE methodology, RSEs can be developed that provide analysts a fast and easy way to screen large numbers of environments and assess proposed changes to the RSE factors.

Miller, Stephen W.↗

DOE OSTI2025

Molecular property prediction for very large databases with natural language processing: a case study in ionic liquid design

The prospect of using artificial intelligence (AI) to accurately screen very large databases of compounds for multiple properties has yet to be realized. Here, we explore this possibility using ionic liquids (ILs) which offer unique physicochemical properties and excellent tunability, making them highly versatile solvents for various research applications. Screening millions of potential ILs for the best perfomance for use in specific tasks with experimental methods alone however, is impractical. Further, traditional’ physics-based computational chemistry is hindered by high computational cost. To address this challenge, we leverage a natural language processing (NLP)-based molecular embedding technique with advanced machine learning (ML) models to predict seven key IL properties: viscosity, density, ionic conductivity, surface tension, melting temperature, toxicity, and water solubility. Comprehensive datasets for these properties are obtained, then NLP featurization with Mol2vec is compared with other featurization techniques such as 2D Morgan fingerprints, and 3D quantum chemistry-derived sigma profiles. NLP-based featurization exhibited the best predictive performance, achieving the highest R 2 and lowest RMSE values for all the studied IL properties. Further, we present case studies of how ILs might be screened using combined property criteria for practical cases – lignocellulosic biomass processing, CO 2 capture, and optimal electrolytes for batteries – screening a novel database of ∼10.6 million generated feasible ILs. The results introduce NLP as a powerful tool for engineering many designer solvents with desirable properties for task specific applications.

Mohan, Mood [Oak Ridge National Laboratory (ORNL),↗

DOE OSTI2024

Catalyst-Specific Accelerated Stress Tests in Proton Exchange Membrane Low-Temperature Electrolysis for Intermittent Operation

Device-level stress tests are developed that focus on anode catalyst layer degradation and future anticipated operating conditions, including intermittent load and reduced platinum group metal content. Square-wave cycles with an upper load limit of 2.5 A cm -2 are utilized to screen commercial iridium (Ir) materials. Performance losses are primarily due to decreasing kinetics and are accompanied by catalyst migration into the membrane, worsening catalyst/ionomer integration, and weakening of the catalyst/membrane interface. For ruthenium-containing catalysts, the in situ performances are higher but durabilities lower than Ir baselines, and any performance advantage is lost within the test. Increased loss is likely due to the higher dissolution rate; microscopy confirmed greater degrees of ruthenium migration. For Ir metal or mixed oxides, ex situ activity improvements generally did not translate to in situ performance. The durability, however, is significantly lower and the loss rate increased from 3 (oxide) to 9 (metal) μ V cycle -1 . These results are consistent with historical findings in literature, rationalize the continued use of iridium oxide as a baseline catalyst, and demonstrate that traditional catalyst development approaches may not improve device-level durability when focused on low-cost applications. A shift in focus may therefore be more effective at improving catalyst utilization and lessening load requirements.

08 HYDROGEN↗

NASA NTRS1983

Radiant heating tests of several liquid metal heat-pipe sandwich panels

Integral heat-pipe sandwich panels, which synergistically combine the thermal efficiency of heat pipes and the structural efficiency of honeycomb sandwich construction, were conceived as a means of alleviating thermal stress problems in the Langley Scramjet Engine. Test panels which utilized two different wickable honeycomb cores, facesheets with screen mesh sintered to the internal surfaces, and a liquid metal working fluid (either sodium or potassium) were tested by radiant heating at various heat-load levels. The heat-pipe panels reduced maximum temperature differences by 31 percent with sodium working fluid and 45 percent with potassium working fluid. Results indicate that a heat-pipe sandwich panel is a potential, simple solution to the engine thermal stress problem. Other interesting applications of the concept include: cold plates for electronic component and circuit card cooling, radiators for large space platforms, low-distortion large area structures (e.g., space antennas) and laser mirrors.

Camarda, C. J.↗

NASA NTRS1983

Development of a Polysilicon Process Based on Chemical Vapor Deposition of Dichlorosilane in an Advanced Siemen's Reactor

Dichlorosilane (DCS) was used as the feedstock for an advanced decomposition reactor for silicon production. The advanced reactor had a cool bell jar wall temperature, 300 C, when compared to Siemen's reactors previously used for DCS decomposition. Previous reactors had bell jar wall temperatures of approximately 750 C. The cooler wall temperature allows higher DCS flow rates and concentrations. A silicon deposition rate of 2.28 gm/hr-cm was achieved with power consumption of 59 kWh/kg. Interpretation of data suggests that a 2.8 gm/hr-cm deposition rate is possible. Screening of lower cost materials of construction was done as a separate program segment. Stainless Steel (304 and 316), Hastalloy B, Monel 400 and 1010-Carbon Steel were placed individually in an experimental scale reactor. Silicon was deposited from trichlorosilane feedstock. The resultant silicon was analyzed for electrically active and metallic impurities as well as carbon. No material contributed significant amounts of electrically active or metallic impurities, but all contributed carbon.

Arevidson, A. N.↗

NASA NTRS1986

Preliminary test data using the MOS DRO with Si:In detector material

The initial testing performed on the Hughes Metal Oxide Semiconductor Direct Readout (MOS DRO) with a Si:In extrinsic infrared array is described. The testing to date was of a screening nature and the results are primarily qualitative rather than quantitative. At a later date the performance optimization phase will be initiated. An encouraging result is that this response is strongly dependent on the detector temperature, to the extent that thermal transients introduced during the chip readout will affect the performance. A responsivity of 1 A/W at 2.2 microns with a bias of 15 volts, which is well below what is optimum bias, was obtained.

Fowler, A. M.↗

NASA NTRS1992

Sliding durability of candidate seal fiber materials in hydrogen from 25 to 900 C

Sliding durability studies of candidate ceramic fibers were conducted in hydrogen to support the high temperature seal development program at NASA LeRC. Pin-on-disk tests were used to measure the friction and durability of a tow or bundle of ceramic fibers in sliding against a superalloy disk. This procedure was used previously to test candidate fibers in an air environment. The fibers based upon mullite (Al2O3-SiO2) chemistry (Nextel 550, 440, and 312) exhibited better durability in hydrogen than in air. HPZ, a complex silicon carboxynitride fiber which showed good durabilty in air, however, showed a significant loss of durability in hot hydrogen. These results are consistent with recent thermodynamic and experimental studies of ceramic compatibility with hydrogen at elevated temperatures. These research results indicate that only oxide fibers display good durability in both air and hydrogen environments. Also, simple, low cost testing in air can provide an adequate data base for initial seal material screening and selection, especially for oxide fiber candidates. The findings of this research provide critical input to the seal design team.

Dellacorte, Christopher↗

DOE OSTI2021

Expanding thermite reactions for downhole applications

Methods and apparatus for forming platforms and flow control features in underground wells is described, using modified thermite reactions to form a ceramic plug in place. The reactive package is engineered to expand laterally, filling the well, and may be used to form a ceramic bridge plug, porous ceramic screen sections, or mitigate lost circulation of drilling fluids. These objectives are achieved through the design of the reactive package and through use of carefully chosen reaction additives that control the molten product rheology, solidification temperature, and pore generations and sustainment.

Lowry, William Edward↗

DOE OSTI2021

Enhanced resistance to decay of imprinted nanopatterns in thin films by bare nanoparticles compared to polymer-grafted nanoparticles

We extend a previous study on the influence of nanoparticles on the decay of nanoimprinted polymer film patterns to compare the effects of “bare” silica (SiO 2 ) nanoparticles and SiO 2 nanoparticles with grafted polymer layers having the same chemical composition as the polymer matrix. This method involves nanoimprinting substrate-supported polymer films using a pattern replicated from a digital versatile disc (DVD), and then annealing the patterned polymer nanocomposite films at elevated temperatures to follow the decay of the topographic surface pattern with time by atomic force microscopy imaging after quenching. We quantified the relaxation of the pattern height (“slumping”) and determined the relaxation time τ for this pattern decay process as a function of nanoparticle filler type and concentration to determine how nanoparticle additives influence relative film stability. Attractive interactions between the bare nanoparticles and the polymer matrix significantly enhance the thermal resilience of the nanopatterns to decay, compared to those of the particle brushes, wherein the particle core interactions are screened from the matrix via the brush layer. A novel aspect of this method is that it readily lends itself to in situ film relaxation measurements in a manufacturing context. We observe that the relaxation time of the pattern relaxation exhibits entropy–enthalpy compensation in the free energy parameters governing the pattern relaxation process as a function of temperature, irrespective of the NP system used, consistent with our previous experimental and computational studies.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Identification of Colletotrichum aenigma as the new causal agent of leaf blight disease on Aucuba japonica Thunb., and screenings of effective fungicides for its sustainable management

Aucuba japonica Thunb is an evergreen woody ornamental plant with significant economic and ecological values. It also produces aucubin, showing a variety of biological activities. It is widely planted in the southwest region of China, including karst landscape areas in Guizhou Province. In January 2022, a serious leaf blight disease was observed on the leaves of A. japonica in the outdoor gardens of Guizhou University, Guiyang, Guizhou, China. The causal agent was identified as Colletotrichum aenigma through amplification and sequencing of the internal transcribed spacer (ITS) region, translation of the chitin synthase ( CHS ) and actin ( ACT ) genes, and morphological characterizations. Koch’s postulates were confirmed by its pathogenicity on healthy leaves, including re-isolation and identification. To our knowledge, this is the first report of C. aenigma causing leaf blight on A. japonica worldwide. To identify pathogen characteristics that could be utilized for future disease management, the effects of temperature and light on mycelial growth, conidia production, and conidial germination, and the effects of humidity on conidial germination were studied. Optimal temperatures for mycelial growth of C. aenigma BY827 were 25–30°C, while 15°C and 35°C were favorable for conidia production. Concurrently, alternating 10-h light and 14-h dark, proved to be beneficial for mycelial growth and conidial germination. Additionally, conidial germination was enhanced at 90% humidity. In vitro screenings of ten chemical pesticides to assess their efficacy in suppressing C. aenigma representative strain BY827. Among them, difenoconazole showed the best inhibition rate, with an EC 50 (concentration for 50% of maximal effect) value of 0.0148 μg/ml. Subsequently, field experiment results showed that difenoconazole had the highest control efficiency on A. japonica leaf blight (the decreasing rate of disease incidence and decreasing rate of disease index were 44.60 and 47.75%, respectively). Interestingly, we discovered that C. aenigma BY827 may develop resistance to mancozeb, which is not reported yet among Colletotrichum spp. strains. In conclusion, our study provided new insights into the causal agent of A. japonica leaf blight, and the effective fungicides evaluated provided an important basis and potential resource for the sustainable control of A. japonica leaf blight caused by C. aenigma in the field.

Fan, Ruidong↗

DOE OSTI2021

Fluctuations analysis of spin susceptibility: Néel ordering revisited in dynamical mean field theory

Here in this paper, we revisit the antiferromagnetic (AF) phase diagram of the single-band three-dimensional half-filled Hubbard model on a simple cubic lattice studied within the dynamical mean field theory (DMFT). Although this problem has been investigated extensively in the literature, a comprehensive understanding of the impact of the different one- and, in particular, two-particle local correlation functions of DMFT on the AF transition temperature is still missing. We have, hence, performed a fluctuation analysis of T N with respect to different local bosonic fluctuations (charge, spin, particle-particle) contained in the two-particle vertex of DMFT. Our results indicate that, beyond weak coupling, the screening of the DMFT vertex by local fluctuations leads to an enhancement of T N with respect to a random phase approximation (RPA) like calculation where this vertex is replaced by the bare interaction. The overall suppression of T N in DMFT with respect to RPA is then solely due to the incoherence introduced by the DMFT self-energy in the one-particle Green's functions. This illustrates the Janus-faced role of the local moment formation in the DMFT solution of the Hubbard model, which leads to completely opposite effects in the one- and two-particle correlation functions.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Predicting path-dependent diffusion barrier spectra in vast compositional space of multi-principal element alloys via convolutional neural networks

The emergent multi-principal element alloys (MPEAs) provide a vast compositional space to search for novel materials for technological advances. How to screen promising compositions from such an ample design space for targeted properties is a grand challenge. In this report, we demonstrate the state-of-the-art deep learning technology—convolutional neural network (CNN)—in predicting path-dependent vacancy migration energy barrier spectra, which are critical to diffusion behavior and many high-temperature properties, in the hyperdimensional composition space of MPEAs. The developed CNN model, fully capturing local chemical features surrounding each vacancy, accurately and efficiently predicts migration energy barrier of MPEAs with different degrees of chemical short-range order and at any unseen compositions. By varying the size of the local region encapsulating vacancy in the CNN model, we reveal that the length scale influencing vacancy migration is surprisingly extensive, up to its six nearest neighboring shells. The efforts of the CNN model make it promising for developing a database of diffusion barriers for various MPEA systems, which would have profound implications for accelerating alloy screening and discovering new compositions with desirable properties.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Molecular fingerprint and machine learning to accelerate design of high-performance homochiral metal–organic frameworks

In this report computational screening was employed to calculate the enantioseparation capabilities of 45 functionalized homochiral metal–organic frameworks (FHMOFs), and machine learning (ML) and molecular fingerprint (MF) techniques were used to find new FHMOFs with high performance. With increasing temperature, the enantioselectivities for (R,S)-1,3-dimethyl-1,2-propadiene are improved. The “glove effect” in the chiral pockets was proposed to explain the correlations between the steric effect of functional groups and performance of FHMOFs. Moreover, the neighborhood component analysis and RDKit/MACCS MFs show the highest predictive effect on enantioselectivities among the four ML classification algorithms with nine MFs that were tested. Based on the importance of MF, 85 new FHMOFs were designed, and a newly designed FHMOF, NO 2 -NHOH-FHMOF, with high similarity to the optimal MFs achieved improved chiral separation performance, with enantioselectivities of 85%. The design principles and new chiral pockets obtained by ML and MFs could facilitate the development of new materials for chiral separation.

molecular fingerprint↗