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DOE OSTI2023

High-throughput ab initio reaction mechanism exploration in the cloud with automated multi-reference validation

Quantum chemical calculations on atomistic systems have evolved into a standard approach to studying molecular matter. These calculations often involve a significant amount of manual input and expertise, although most of this effort could be automated, which would alleviate the need for expertise in software and hardware accessibility. Here, we present the AutoRXN workflow, an automated workflow for exploratory high-throughput electronic structure calculations of molecular systems, in which (i) density functional theory methods are exploited to deliver minimum and transition-state structures and corresponding energies and properties, (ii) coupled cluster calculations are then launched for optimized structures to provide more accurate energy and property estimates, and (iii) multi-reference diagnostics are evaluated to back check the coupled cluster results and subject them to automated multi-configurational calculations for potential multi-configurational cases. All calculations are carried out in a cloud environment and support massive computational campaigns. Key features of all components of the AutoRXN workflow are autonomy, stability, and minimum operator interference. We highlight the AutoRXN workflow with the example of an autonomous reaction mechanism exploration of the mode of action of a homogeneous catalyst for the asymmetric reduction of ketones.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2018

Prediction of Cyclic Variability and Knock-Limited Spark Advance (KLSA) in Spark-Ignition (SI) Engine

Engine knock remains one of the major barriers to further improve thermal efficiency of Spark Ignition (SI) engines. Knock can be suppressed by lowering the compression ratio, or retarding the spark ignition timing, however, at an expense of efficiency penalty. SI engine is usually operated at knock-limited spark advance (KLSA) to achieve possibly maximum efficiency with given engine hardware and fuel properties, such as Research Octane Number (RON), Motor Octane Number (MON), and heat of vaporization, etc. Co-optimization of engine design and fuel properties is promising to improve the engine efficiency and predictive CFD models can be used to facilitate this optimization process. However, difficulties exist in predicting KLSA in CFD simulations. First, cyclic variability of SI engine demands that multi-cycle results are required to capture the extreme conditions. Secondly, Mach Courant-Friedrichs-Lewy (CFL) number of 1 is desired to accurately predict the knock intensity (KI), resulting in unaffordable computational cost, especially for multi-cycle simulations. In this study, a new approach to numerically predict KLSA using large Mach CFL number of 50 is proposed. This approach is validated against experimental data for a boosted Direct Injection Spark Ignition (DISI) engine at multiple loads and spark timings. G-equation combustion model coupled with well-mixed chemical kinetic model are used to predict the turbulent flame propagation and end-gas auto-ignition, respectively. Simulations run for 10 consecutive engine cycles at each condition. The results show good agreement between model predictions and experiments in terms of cylinder pressure, combustion phasing and cyclic variation. Engine knock is predicted with early spark ignition timing, indicated by significant pressure wave oscillation and end-gas heat release. Maximum Amplitude of Pressure Oscillation (MAPO) analysis is performed to quantify the KI, and the slope change point in KI extrema is used to indicate the KLSA accurately. Using a smaller Mach CFL number of 5 also results in the same conclusions thus demonstrating that this approach is insensitive to the Mach CFL number. The use of large Mach CFL number allows us to achieve fast turn-around time for multi-cycle engine CFD simulations.

Yue, Zongyu↗

DOE OSTI2018

Micro-crack Pinning and Interfacial Fracture in Mixed Metal Oxide Reinforced Epoxy Nanocomposite

The effects of mixed metal oxides (Ce{sub x}Zr{sub 1−x}O{sub 2}) nanoparticle dispersion on the mechanical properties and fracture mechanisms of epoxy polymer matrix and its composite with glass fiber system are reported in this paper. Ce{sub x}Zr{sub 1−x}O{sub 2} nanoparticles are synthesized using sol–gel method, and its crystallinity is optimized. Epoxy nanocomposites are synthesized by a dispersion technique, and the compressive properties are optimized. The glass transition temperature has been improved. Epoxy with 5 wt.% of Ce{sub 0.75}Zr{sub 0.25}O{sub 2} shows optimal results with an increase of 17.4% in compressive modulus, and an increase of 23.4% in compressive strength with respect to those of neat epoxy. This improvement attributes to higher strength of shearing of polymer during the fracture of the nanoparticle interface. Micro-crack kinks at the interfaces can further delay fracture under compression. E-glass fabric-reinforced 5 wt.% Ce{sub 0.75}Zr{sub 0.25}O{sub 2}–epoxy nanocomposite also shows improvement in the mechanical properties via nanoscale interface with fibers. This type of ceramic nanocomposites has useful applications in thermal/electrical insulations besides improving the compressive/buckling properties.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2017

Machine Learning Force Field Parameters from Ab Initio Data

Machine learning (ML) techniques with the genetic algorithm (GA) have been applied to determine a polarizable force field parameters using only ab initio data from quantum mechanics (QM) calculations of molecular clusters at the MP2/6-31G(d,p), DFMP2(fc)/jul-cc-pVDZ, and DFMP2(fc)/jul-cc-pVTZ levels to predict experimental condensed phase properties (i.e., density and heat of vaporization). The performance of this ML/GA approach is demonstrated on 4943 dimer electrostatic potentials and 1250 cluster interaction energies for methanol. Excellent agreement between the training data set from QM calculations and the optimized force field model was achieved. Our results were further improved by introducing an offset factor during the machine learning process to compensate for the discrepancy between the QM calculated energy and the energy reproduced by optimized force field, while maintaining the local “shape” of the QM energy surface. Throughout the machine learning process, experimental observables were not involved in the objective function, but were only used for model validation. The best model, optimized from the QM data at the DFMP2(fc)/jul-cc-pVTZ level, appears to perform even better than the original AMOEBA force field (amoeba09.prm), which was optimized empirically to match liquid properties. The present effort shows the possibility of using machine learning techniques to develop descriptive polarizable force field using only QM data. The ML/GA strategy to optimize force fields parameters described here could easily be extended to other molecular systems.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

A comprehensive review on valorization of chestnut processing wastes into bio‐based composites and bioplastics

Abstract This review examines the characterization and utilization of chestnut processing wastes (35%) in the production of bioplastics and biocomposites. In this review, a Web of Science search without any publishing year restriction on the biochemical compositions of all the components of Castanea sativa . The obtaining of bioplastics and biocomposites based on C. sativa was reviewed. First, it highlights the biochemical composition and antioxidant properties of chestnut fruit, shell, burrs, leaves, flowers, and wood focusing on the most important compounds, such as phenolic acids, flavonoids, carbohydrates, Klason lignin, cellulose, and glucan, which can enhance the properties of these materials. Then the review covers using several chestnut extracts and fillers in bioplastics production through solvent casting technique. The mechanical, structural, bioactive properties, and moisture content were optimized through the composition and production. The color, UV absorption, antioxidant, and antimicrobial activity were also discussed. Biocomposites reinforced with chestnut burs, shells, or wood flour increased the intended properties. The enhancements in tensile strength, elastic modulus, and the effects of a pre‐treatment were evaluated. Additionally, it discusses material recovery, recycling, and reuse, particularly how it affects the biodegradability of composites incorporating chestnut waste residues. Highlights Chestnut fruit, shells, and burrs are rich in starch, lignin, and cellulose. The waste of chestnut processing can be used in bioplastics and biocomposites. Chestnut‐based films and biocomposites exhibit promising mechanical properties. The antimicrobial activity, making films, and composites proper for food packaging. New techniques boost performance, offering alternatives to conventional plastics.

Silva, Simão B. [REQUIMTE/LAQV, ISEP, Polytechnic ↗

DOE OSTI2022

Interplay of strain and phase evolution of laser powder bed fusion Ti–6Al–4V

While additive manufacturing (AM) provides a method of producing geometrically complex and highly detailed structures, the generation of residual strain in AM processes like laser powder bed fusion (L-PBF) can negatively impact performance-enabling properties. In applications such as orthopedic implants, specific performance windows require optimized microstructures in order to obtain desirable properties from multi-phase alloys like Ti–6Al–4V. This research aims to quantify the microscale origins of strain in L-PBF manufactured Ti–6Al–4V by understanding how strain is distributed at the grain and sub-grain scale, the interplay between phase evolution and strain, and examining post-processing strain relief strategies to control these features. Model spinal cage implants were manufactured from Ti–6Al–4V powder via L-PBF and then subjected to strain relieving heat treatment cycles above and below the Ti–6Al–4V β transus as a function of time and cooling rate. Residual strain was then studied via high resolution electron backscatter diffraction (HR-EBSD), and 2D strain maps with sub-micron resolution were generated for each post-processing state. It was found that macroscale thermal strains decreased with heat treatment time, but additional contributions from phase stabilizing residual strains retained primarily in the α' grains as lattice distortive strain remained. Additionally, the retention of β phase significantly changed the strain and dislocation distribution while reducing overall residual strain. In conclusion, these results were validated and reinforced with 3D mesoscopic micromechanical modeling of strain behavior across simulated microstructures, confirming that the local lattice dilation of α’ martensite is a primary contributor of microscale strain generation and retention in L-PBF Ti–6Al–4V.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Lighting up hot stuff

Plasmonic heating by nanoparticles has been used to promote a range of chemical reactions. Now, thermoplasmonic activation has been applied to latent ruthenium catalysts, enabling olefin metathesis initiated by visible and infrared light. Additionally, the desire to harness light to drive chemical transformations has surely existed as long as the study of chemistry itself. In the earliest documented applications, light was used simply as a heat source — for example, in the distillation of liquids. Since that time, our knowledge of how light and matter interact has increased exponentially, with greater mechanistic and molecular understanding enabling modern photochemists to design molecules with a myriad of finely tuned optical properties for catalysis, biochemistry, optoelectronics and more. Nonetheless, the design and optimization of molecules to achieve specific optical properties is still challenging, and for some applications, a return to the ‘simplest’ transformation — that of light to heat — can offer a more efficient approach to achieve light-mediated chemical reactions. Now, writing in Nature Chemistry, Yossi Weizmann and colleagues describe a strategy for organic and polymer synthesis driven by the conversion of light to heat.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2014

Crystal structure and high temperature transport properties of Yb-filled p-type skutterudites Yb{sub x}Co{sub 2.5}Fe{sub 1.5}Sb{sub 12}

Partially Yb-filled Fe substituted polycrystalline p-type skutterudites with nominal compositions Yb{sub x}Co{sub 2.5}Fe{sub 1.5}Sb{sub 12}, with varying filler concentrations x, were synthesized by reacting the constituent elements and subsequent solid state annealing, followed by densification by hot-pressing. The compositions and filling fractions were confirmed with a combination of Rietveld refinement and elemental analysis. Their thermoelectric properties were evaluated from 300 to 800 K. The Seebeck coefficients for the specimens increase with increasing temperature and plateau at around 750 K. The thermal conductivity decreases with increasing Yb filling fraction, and bipolar conduction becomes evident and increases at elevated temperatures. A maximum ZT value of 0.8 was obtained at 750 K for Yb{sub 0.47}Co{sub 2.6}Fe{sub 1.4}Sb{sub 12}. The thermoelectric properties and potential for further optimization are discussed in light of our results. - Graphical abstract: Display Omitted - Highlights: • Yb-filled Fe-substituted p-type CoSb{sub 3} skutterudites, Yb{sub x}Co{sub 2.5}Fe{sub 1.5}Sb{sub 12}, were structurally and physically characterized. • Both filling fraction and Co-to-Fe ratio affect the transport properties. • High ZT was obtained for the composition with ∼50% Yb filling and a slightly lower than nominal Fe content.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2017

Enhanced photocatalytic degradation of methylene blue: Preparation of TiO{sub 2}/reduced graphene oxide nanocomposites by direct sol-gel and hydrothermal methods

Highlights: • TiO{sub 2}-RGO nanocomposites presented, in all the cases studied, improved photocatalytic properties compared to bare TiO{sub 2}. • An optimal composition, in terms of TiO{sub 2}/RGO ratio was found in both series nanocomposites. • Nanocomposites of sol-gel method presented improved textural properties than those of hydrothermal method. • Optimization in the preparation, needed to obtain materials with finer properties for effective application in environmental. - Abstract: In this study, two different preparation methods of titanium dioxide nanoparticles/reduced graphene oxide nanocomposites were investigated using direct sol-gel method followed by hydrothermal treatment or simple hydrothermal route. A different amount of graphene (1- 20%) was mixed with TiO{sub 2} for both series of samples in order to improve the photocatalytic activity. The influence of the preparation method on the physico-chemical properties was established by different characterization methods and the photocatalytic degradation of methylene blue (MB) under UV light irradiation was used as test reaction. The highest photocatalytic activity was observed for the nanocomposites containing 10 wt% of graphene. The elimination of MB can reach 93% and 82% for the nanocomposites with 10 wt% graphene prepared by the sol-gel and hydrothermal methods, respectively. These photocatalysts are promising for practical application in nanotechnology.

36 MATERIALS SCIENCE↗

DOE OSTI2017

Strain-engineered optoelectronic properties of 2D transition metal dichalcogenide lateral heterostructures

Compared with their bulk counterparts, 2D materials can sustain much higher elastic strain at which optical quantities such as bandgaps and absorption spectra governing optoelectronic device performance can be modified with relative ease. Using first-principles density functional theory and quasiparticle GW calculations, we demonstrate how uniaxial tensile strain can be utilized to optimize the electronic and optical properties of transition metal dichalcogenide lateral (in-plane) heterostructures such as MoX 2 /WX 2 (X = S, Se, Te). We find that these lateral-type heterostructures may facilitate efficient electron–hole separation for light detection/harvesting and preserve their type II characteristic up to 12% of uniaxial strain. Based on the strain-dependent bandgap and band offset, we show that uniaxial tensile strain can significantly increase the power conversion efficiency of these lateral heterostructures. Our results suggest that these strain-engineered lateral heterostructures are promising for optimizing optoelectronic device performance by selectively tuning the energetics of the bandgap.

2D materials↗

DOE OSTI2023

Optimizing chemistry for designing oxidation resistant FeCrAl alloys

Abstract Traditionally, FeCrAl alloys played an important role in high-temperature applications due to their ability to form a passive Al oxide film at temperatures above ~ 800 °C. Recently, FeCrAl alloys became of interest for the application of accident tolerant nuclear fuel cladding. This study covers work done at GE Research for better understanding the role of Al, Cr, and Mo in oxidation kinetics and thermodynamics. Several models and commercial prototype alloys have been tested in hydrothermal corrosion autoclave loops, at low temperature steam exposure (~ 400 °C), high temperature steam exposure (~ 1000 °C or higher), and high temperature air exposures. The results provide insights on how chromium and aluminum play a significant role in both high temperature and low temperature oxidation of FeCrAl. Additionally, machine learning tools are used to gain further insights on both predicting future optimized chemistries for balancing the properties of hydrothermal corrosion, low and high temperature steam oxidation, and thermal aging (which is exacerbated due to radiation in a nuclear reactor environment). GE plans to use this framework to further optimize the FeCrAl alloy system for use in nuclear reactor environments. Graphical abstract

Roy, Indranil (ORCID:0000000336124323)↗

DOE OSTI2025

Quantum annealing-assisted lattice optimization

High Entropy Alloys (HEAs) have drawn great interest due to their exceptional properties compared to conventional materials. The configuration of HEA system is considered a key to their superior properties, but exhausting all possible configurations of atom coordinates and species to find the ground energy state is extremely challenging. In this work, we proposed a quantum annealing-assisted lattice optimization (QALO) algorithm, which is an active learning framework that integrates the Field-aware Factorization Machine (FFM) as the surrogate model for lattice energy prediction, Quantum Annealing (QA) as an optimizer and Machine Learning Potential (MLP) for ground truth energy calculation. By applying our algorithm to the NbMoTaW alloy, we reproduced the Nb depletion and W enrichment observed in bulk HEA. We found our optimized HEAs to have superior mechanical properties compared to the randomly generated alloy configurations. Our algorithm highlights the potential of quantum computing in materials design and discovery, laying a foundation for further exploring and optimizing structure-property relationships.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Toward a tunable fabrication of multifunctional iron-aluminum spinels via solution combustion synthesis: The effects of fuel, heating mode, and Fe:Al precursor ratio

The solid solutions of iron and aluminum spinels (hercynite, maghemite, magnetite, and γ-alumina) are promising materials for emerging technologies such as solar thermochemical fuel production and clean dehydrogenation of fossil fuels. Solution combustion synthesis (SCS) is an attractive technique for the fabrication of these materials as it has been used for synthesis of many nanoscale oxides. However, the design space of SCS is large with many synthesis parameters affecting the properties of the combustion products. To optimize the SCS of these materials, it is important to determine which parameters yield the best properties. In the present work, FeAlO x nanocomposites were obtained by SCS using iron nitrate and aluminum nitrate as precursors and oxidizers. Two fuels (citric acid and glycine), three Fe:Al molar ratios (1:2, 1:1, and 2:1) in the precursors, and three heating modes (hotplate, muffle furnace, and microwave oven) were compared. The products were characterized by X-ray diffraction analysis, scanning electron microscopy, Brunauer-Emmett-Teller surface area analysis, and laser diffraction particle size analysis. The fuel used had the largest impact on the combustion behavior and hence the material properties. The high combustion temperature of glycine allowed for the formation of an FeAl 2 O 4 /Fe 3 O 4 solid solution, while the lower-temperature combustion of citric acid yielded Al-substituted γ-Fe 2 O 3 with high amorphous content. With citric acid, the specific surface area as high as 200 m 2 /g was achieved. The effect of Fe:Al precursor ratio was clearly seen in variations of the lattice parameter, which demonstrated the ability to tune the phase composition. The heating mode had minor effects on material properties, but some differences were observed. As a result, all products obtained by SCS in a microwave oven had low specific surface areas compared to those obtained with a hotplate and a muffle furnace.

Alumina↗

DOE OSTI2026

Database and deep-learning scalability of anharmonic phonon properties by automated brute-force first-principles calculations

Understanding the anharmonic phonon properties of crystal compounds—such as phonon lifetimes and thermal conductivities—is essential for investigating and optimizing their thermal transport behaviors. These properties also impact optical, electronic, and magnetic characteristics through interactions between phonons and other quasiparticles and fields. In this study, we develop an automated first-principles workflow to calculate anharmonic phonon properties and build a comprehensive database encompassing more than 6500 inorganic compounds. Utilizing this dataset, we train a graph neural network model to predict thermal conductivity values and spectra from structural parameters, demonstrating a scaling law in which prediction accuracy improves with increasing training data size. High-throughput screening with the model enables the identification of materials exhibiting extreme thermal conductivities—both high and low. The resulting database offers valuable insights into the anharmonic behavior of phonons, thereby accelerating the design and development of advanced functional materials.

Ohnishi, Masato [University of Tokyo (Japan); Inst↗

DOE OSTI2016

An optimal thermal evaporation synthesis of c-axis oriented ZnO nanowires with excellent UV sensing and emission characteristics

Highlights: • c-Axis alignment of ZnO nanowires was optimized using self-seeding thermal evaporation method. • Influence of purified air on the morphology and optoelectronic properties were studied. • Nanowires grown under optimal conditions exhibit strong UV emission peak in PL spectrum. • Optimized growth condition establish nanowires of excellent UV sensing characteristics - Abstract: Well-aligned (c-axis oriented) ZnO nanowire arrays were successfully synthesized on Si (1 0 0) substrates through an optimized self-seeding thermal evaporation method. An open-ended chemical vapor deposition (CVD) setup was used in the experiment, with argon and purified air as reaction gases. Epitaxial growth of c-axis oriented ZnO nanowires was observed for 5 sccm flow rate of purified air, whereas Zn/Zn suboxide layers and multiple polycrystalline layers of ZnO were obtained for absence and excess of purified air, respectively. Ultraviolet (UV) sensing and emission properties of the as-grown ZnO nanostructures were investigated through the current–voltage (I–V) characteristics of the nanowires under UV (λ = 365 nm) illumination of 8 mW/cm{sup 2} and using photoluminescence spectra. Nanowires grown under optimum flow of air emitted four times higher intensity of 380 nm UV light as well as exhibited 34 times higher UV radiation sensitivity compared to that of other nanostructures synthesized in this study.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Non‐Linear Optical Properties of the ( RE ) 3 CuGeS 7 Family of Compounds

Abstract Non‐linear optical materials must possess a balanced combination of laser‐induced damage threshold (LDT) and second‐harmonic generation (SHG) and be phase matchable. In our previous work, chiral and polar La 3 CuGeS 7 was identified as a promising non‐linear optical material. Herein, we report the optimization of non‐linear optical properties through replacement of La with smaller lanthanides. It is determined that Gd 3 CuGeS 7 exhibits the best combination of SHG (1.6× AgGaS 2 at 88–105 μm particle size) and LDT (3× AgGaS 2 , 89 MW/cm 2 ) and is phase matchable. Based on changes in metal‐sulfur bond lengths and angles, we further propose structural optimization through solid‐solution formation and doping.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

A Modified Embedded-Atom Method Potential for a Quaternary Fe-Cr-Si-Mo Solid Solution Alloy

Ferritic-martensitic steels, such as T91, are candidate materials for high-temperature applications, including superheaters, heat exchangers, and advanced nuclear reactors. Considering these alloys’ wide applications, an atomistic understanding of the underlying mechanisms responsible for their excellent mechano-chemical properties is crucial. Here, we developed a modified embedded-atom method (MEAM) potential for the Fe-Cr-Si-Mo quaternary alloy system—i.e., four major elements of T91—using a multi-objective optimization approach to fit thermomechanical properties reported using density functional theory (DFT) calculations and experimental measurements. Elastic constants calculated using the proposed potential for binary interactions agreed well with ab initio calculations. Furthermore, the computed thermal expansion and self-diffusion coefficients employing this potential are in good agreement with other studies. This potential will offer insightful atomistic knowledge to design alloys for use in harsh environments.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2018

Explicit Backscattering Coefficient for Ultrasonic Wave Propagating in Hexagonal Polycrystals with Fiber Texture

Hexagonal polycrystalline materials such as titanium, zirconium and magnesium are common in engineering structures like car bodies and airplane engines. These components are usually specially designed in microstructure level to guarantee desired mechanical properties, so microstructure characterization is of great importance to the optimization of manufacturing processes. Compared with traditional destructive characterization methods like scanning electron microscopy (SEM), the ultrasonic approach is nondestructive and cost-effective. Although some progress has been made in ultrasonic microstructure characterization of polycrystal aggregates of hexagonal grains, some factors like macro texture and grain size distribution associated with realistic microstructures were not accounted for yet. Targeting one common texture component for hexagonal polycrystalline materials, {0001} basal fiber texture, this paper derived the explicit backscattering coefficient for aggregates of ellipsoidal grains through one texture parameter and obtained an analytical backscattering coefficient for microstructures with various grain size distributions. In this study, the basal fiber texture was quantified by the 1D Gaussian orientation distribution function (ODF), which merely includes one texture parameter, and its relationship with generalized spherical harmonics ODF was also addressed. Moreover, explicit expressions for effective elastic moduli and elastic constant covariances were derived. Furthermore, some computational examples were given to demonstrate the impacts of texture, frequency, grain geometry and grain size distribution on backscattering behavior. The theoretical results in this study will greatly benefit the later ultrasonic microstructure characterization of hexagonal polycrystalline materials.

42 ENGINEERING↗