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DOE OSTI2021

Unexpected Formation of Metallofulleroids from Multicomponent Reactions, with Crystallographic and Computational Studies of the Cluster Motion

New multicomponent reactions involving an isocyanide, terminal or internal alkynes, and endohedral metallofullerene (EMF) Lu 3 N@C 80 yield metallofulleroids which are characterized by massspectrometry, HPLC, and multiple 1D and 2D NMR techniques. Single crystal studies revealed one ketenimine metallofulleroid has ordered Lu 3 N cluster which is unusual for EMF monoadducts. Computational analysis, based on crystallographic data, confirm that the endohedral cluster motion is controlled by the position of the exohedral organic appendants. Furthermore, our findings provide a new functionalization reaction for EMFs, and a potential facile approach to freeze the endohedral cluster motion at relatively high temperatures.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Unexpected Formation of Metallofulleroids from Multicomponent Reactions, with Crystallographic and Computational Studies of the Cluster Motion

Abstract New multicomponent reactions involving an isocyanide, terminal or internal alkynes, and endohedral metallofullerene (EMF) Lu 3 N@C 80 yield metallofulleroids which are characterized by mass‐spectrometry, HPLC, and multiple 1D and 2D NMR techniques. Single crystal studies revealed one ketenimine metallofulleroid has ordered Lu 3 N cluster which is unusual for EMF monoadducts. Computational analysis, based on crystallographic data, confirm that the endohedral cluster motion is controlled by the position of the exohedral organic appendants. Our findings provide a new functionalization reaction for EMFs, and a potential facile approach to freeze the endohedral cluster motion at relatively high temperatures.

Li, Yanbang↗

DOE OSTI2024

Enhanced read resolution in reconfigurable memristive synapses for Spiking Neural Networks

Abstract The synapse is a key element circuit in any memristor-based neuromorphic computing system. A memristor is a two-terminal analog memory device. Memristive synapses suffer from various challenges including high voltage, SET or RESET failure, and READ margin issues that can degrade the distinguishability of stored weights. Enhancing READ resolution is very important to improving the reliability of memristive synapses. Usually, the READ resolution is very small for a memristive synapse with a 4-bit data precision. This work considers a step-by-step analysis to enhance the READ current resolution or the read current difference between two resistance levels for a current-controlled memristor-based synapse. An empirical model is used to characterize the $${\hbox {HfO}}_{2}$$ HfO 2 based memristive device. $$1\textrm{st}$$ 1 st and $$2\textrm{nd}$$ 2 nd stage device of our proposed synapse design can be scaled to enhance the READ current margin up to $$\sim$$ ∼ 4.3 $$\times$$ × and $$\sim$$ ∼ 21%, respectively. Moreover, READ current resolution can be enhanced with run-time adaptation techniques such as READ voltage scaling and body biasing. The READ voltage scaling and body biasing can improve the READ current resolution by about 46% and 15%, respectively. TENNLab’s neuromorphic computing framework is leveraged to evaluate the effect of READ current resolution on classification, control, and reservoir computing applications. Higher READ current resolution shows better accuracy than lower resolution even when facing different levels of read noise.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Domain wall-magnetic tunnel junction spin–orbit torque devices and circuits for in-memory computing

There are pressing problems with traditional computing, especially for accomplishing data-intensive and real-time tasks, that motivate the development of in-memory computing devices to both store information and perform computation. Magnetic tunnel junction memory elements can be used for computation by manipulating a domain wall, a transition region between magnetic domains, but the experimental study of such devices has been limited by high current densities and low tunnel magnetoresistance. Here, we study prototypes of three-terminal domain wall-magnetic tunnel junction in-memory computing devices that can address data processing bottlenecks and resolve these challenges by using perpendicular magnetic anisotropy, spin–orbit torque switching, and an optimized lithography process to produce average device tunnel magnetoresistance TMR = 171% and average resistance-area product RA = 29 Ω μm2, close to the RA of the unpatterned film. Device initialization variation in switching voltage is shown to be curtailed to 7%–10% by controlling the domain wall initial position, which we show corresponds to 90%–96% accuracy in a domain wall-magnetic tunnel junction full adder simulation. Repeatability of writing and resetting the device is shown. A circuit shows an inverter operation between two devices, showing that a voltage window is large enough, compared to the variation noise, to repeatably operate a domain wall-magnetic tunnel junction circuit. These results make strides in using magnetic tunnel junctions and domain walls for in-memory and neuromorphic computing applications.

Alamdar, Mahshid (ORCID:0000000221732935)↗

DOE OSTI2020

Tuning Catalytic Sites on Zr 6 O 8 Metal–Organic Framework Nodes via Ligand and Defect Chemistry Probed with tert -Butyl Alcohol Dehydration to Isobutylene

Metal–organic frameworks (MOFs) have drawn wide attention as candidate catalysts, but some essential questions about their nature and performance have barely been addressed. (1) How do OH groups on MOF nodes act as catalytic sites? (2) What are the relationships among these groups, node defects, and MOF stability, and how do reaction conditions influence them? (3) What are the interplays between catalytic properties and transport limitations? To address these questions, we report an experimental and theoretical investigation of the catalytic dehydration of tert -butyl alcohol (TBA) used to probe the activities of OH groups of Zr 6 O 8 nodes in the MOFs UiO-66 and MOF-808, which have different densities of vacancy sites and different pore sizes. The results show that (1) terminal node OH groups are formed as formate and/or acetate ligands present initially on the nodes react with TBA to form esters, (2) these OH groups act as catalytic sites for TBA dehydration to isobutylene, and (3) TBA also reacts to break node–linker bonds to form esters and thereby unzip the MOFs. The small pores of UiO-66 limit the access of TBA and the reaction with the formate/acetate ligands bound within the pores, whereas the larger pores of MOF-808 facilitate transport and favor reaction in the MOF interior. However, after removal of the formate and acetate ligands by reaction with methanol to form esters, interior active sites in UiO-66 become accessible for the reaction of TBA, with the activity depending on the density of defect sites with terminal OH groups. The number of vacancies on the nodes is important in determining a tradeoff between the catalytic activity of a MOF and its resistance to unzipping. Computations at the level of density functional theory show how the terminal OH groups on node vacancies act as Brønsted bases, facilitating TBA dehydration via a carbocation intermediate in an E1 mechanism; the calculations further illuminate the comparable chemistry of the unzipping.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Computational investigation of water glasses using machine-learning potentials

The molecular origins of water’s anomalous properties have long been a subject of scientific inquiry. The liquid–liquid phase transition hypothesis, which posits the existence of distinct low-density and high-density liquid states separated by a first-order phase transition terminating at a critical point, has gained increasing experimental and computational support and offers a thermodynamically consistent framework for many of water’s anomalies. However, experimental challenges in avoiding crystallization near the postulated liquid–liquid critical point have focused attention to water’s canonical glassy states: low-density and high-density amorphous ice. Here, we use two Deep Potential machine-learning models, trained on the Strongly Constrained and Appropriately Normed density functional and the highly accurate Many-Body Polarizable potential, to conduct an investigation of water’s glassy phenomenology based on quantum mechanical calculations. Despite not being explicitly trained on amorphous ices, both models accurately capture the structure and transformation of the water glasses, including their interconversion along different thermodynamic paths. Isobaric quenching of liquid water at various pressures generates a continuum of intermediate amorphous ices and density fluctuations increase near the liquid–liquid critical pressure. The glass transition temperatures of the amorphous ices produced at different pressures exhibit two distinct branches, corresponding to low-density and high-density amorphous ice behaviors, consistent with experiment and the liquid–liquid transition hypothesis. Extrapolating transformation pressures from isothermal compressions to experimental compression rates brings our simulations into excellent agreement with data. Our findings demonstrate that machine-learning potentials trained on equilibrium phases can effectively model nonequilibrium glassy behavior and pave the way for studying long-timescale, out-of-equilibrium processes with quantum mechanical accuracy.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Nitrogen Atom Transfer Catalysis by Metallonitrene C−H Insertion: Photocatalytic Amidation of Aldehydes

Abstract C−H amination and amidation by catalytic nitrene transfer are well‐established and typically proceed via electrophilic attack of nitrenoid intermediates. In contrast, the insertion of (formal) terminal nitride ligands into C−H bonds is much less developed and catalytic nitrogen atom transfer remains unknown. We here report the synthesis of a formal terminal nitride complex of palladium. Photocrystallographic, magnetic, and computational characterization support the assignment as an authentic metallonitrene (Pd−N) with a diradical nitrogen ligand that is singly bonded to Pd II . Despite the subvalent nitrene character, selective C−H insertion with aldehydes follows nucleophilic selectivity. Transamidation of the benzamide product is enabled by reaction with N 3 SiMe 3 . Based on these results, a photocatalytic protocol for aldehyde C−H trimethylsilylamidation was developed that exhibits inverted, nucleophilic selectivity as compared to typical nitrene transfer catalysis. This first example of catalytic C−H nitrogen atom transfer offers facile access to primary amides after deprotection.

Schmidt‐Räntsch, Till↗

DOE OSTI2022

Nitrogen Atom Transfer Catalysis by Metallonitrene C−H Insertion: Photocatalytic Amidation of Aldehydes

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Multisource Mobile Transfer Learning Algorithm Based on Dynamic Model Compression

With the development of the Internet of Things, the application of computer vision on mobile phones is becoming more and more extensive and people have higher and higher requirements for the timeliness of the recognition results returned and the processing capabilities of the mobile phone for image recognition. However, the processing capability and storage capability of the user terminal equipment cannot meet the needs of identifying and storing a large number of pictures, and the data transmission process will cause high energy consumption of the terminal equipment. At the same time, multisource deep transfer learning has outstanding performance in computer vision and image classification. However, due to the huge amount of calculation of the deep network model, it is impossible to use the existing excellent network model to realize image recognition and classification on the mobile terminal. In order to solve the abovementioned problems, we propose a multisource mobile transfer learning algorithm based on dynamic model compression, this algorithm considers the realization of multisource transfer learning computing in the case of multiple mobile device computing source domains, and the method also guarantees data privacy and security for each device (origin domain). Meanwhile, extensive experiments show that our method can achieve remarkable results in popular image classification datasets.

Gao, Peng↗

DOE OSTI2022

Te/CdTe and Al/CdTe Interfacial Energy Band Alignment by Atomistic Modeling

A synergistic approach that incorporates first-principles atomistic modeling with numerical device simulations is used to systematically evaluate the role of heterointerfaces within metal-chalcogenide-based photovoltaic technologies. Two interfaces involving either a tellurium back contact or aluminum back electrode combined with a cadmium telluride absorber layer within cadmium-telluride-based thin-film solar cells are investigated on an atomic scale to determine the mechanisms contributing to variations in device performance. Electronic structures and predicted charge transport behavior with respect to cadmium and tellurium termination of the absorber layer are studied along the polar oriented CdTe{111} facets. The computational methodology reveals a noticeable contrast between the Schottky barrier forming Al/CdTe interface versus the Type I Te/CdTe heterojunction. Greater band bending features are exhibited by the cadmium termination as opposed to the tellurium termination for each interface case. Subsequent device modeling suggests that 3.6% higher photovoltaic conversion efficiency is achievable for the cadmium termination relative to the tellurium termination of the Te/CdTe interface. Based strictly on an idealistic representation, both interface models show the importance of atomic-scale interfacial properties for cadmium telluride solar cell device performance with their bulk properties being validated in comparison to published experimental data. Here, the synergistic approach offers a suitable method to analyze solar cell interfaces through a predictive computational framework for the engineering and optimization of metal-chalcogenide-based thin-film photovoltaic technologies.

Green’s function↗

DOE OSTI2025

Capacity Design Method for Integrated Convective/Radiant Terminals to Guarantee Overall and Local Environment

To achieve energy efficiency in the operation of heating devices and ensure thermal comfort in indoor environments, the integrated convective/radiant terminals have become an important development direction for heating systems. This study selected a 14 m 2 bedroom hot summer and cold winter region in China as a case study. Computational Fluid Dynamics (CFD) methodology was used to investigate the design of integrated convective/radiant terminals to ensure thermal comfort for both the entire room and partial space. The results indicate that focusing on partial space during the steady-state stage can achieve energy savings of 31.1% compared to guaranteeing the entire room. Additionally, during the start-up stage, there is a significant reduction of convective unit capacity by 17.6% when the start-up time is 15 minutes. These findings provide data support for the design and engineering applications of products of integrated convective/radiant terminals. Besides, the excess convection ratios for the entire room and partial space are specified as 0.69 to 0.80 and 0.73 to 0.83, respectively.

Chi, Junjie↗

DOE OSTI2023

Interaction energy and isosteric heat of adsorption between hydrogen and magnesium diboride

Hydrogen storage materials form a crucial research topic for future energy utilization employing hydrogen and among those of interest magnesium diboride (MgB 2 ) has shown its prevalence. In this study, a first-principles analytical adsorption model of one hydrogen molecule in the vicinity of various magnesium diboride crystal surfaces was developed in order to obtain surface thermodynamic properties as a function of molecular and lattice properties. Henry's law constant (K H ) and isosteric heat of adsorption (ΔH ads ) indicators of the affinity between a gaseous molecule and a solid surface are thus calculated. The results in this paper not only address questions pertaining to the first stage of hydrogen storage processes but also advance the understanding of physisorption thermodynamics of a neutral molecule (H 2 ) coming in contact with a layered metallic-like surface (MgB 2 ). Although the model is built from a framework of classical calculations, quantum effects are incorporated as the fractional charge of the ions on the free surfaces, which is essential for the calculation of analytic thermodynamic values that approximate calculations from other methods. To benchmark our theoretical models, periodic density functional calculations were performed to determine the interactions between H 2 and different MgB 2 surfaces from first-principles. By considering both the top and sublayers of MgB 2 in calculating interaction energy, we have analytically and computationally calculated the interaction energies of H 2 molecules and MgB 2 's terminated planes, and witnessed the strong dependence of interaction energies on surface charges. We have also observed a dipole flipping phenomenon which explains the discontinuity seen in the interaction energy graph of Mg(0001). Both analytical and computational results showed heat of adsorption at zero coverage varying at a very low range (<7 kJ mol -1 ).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Inverse Reinforcement Learning based Bayesian Goal Inference Method for Early Nuclear Proliferation Detection

Traditional methods for detection of nuclear proliferation indicators are usually applied after nuclear proliferation has already occurred. There is a need to advance these methods to perform early detection of nuclear proliferation indicators. In this project, we formulated an early detection problem as a sequential, decision-making, goal inference problem based on research publications of authors, to determine whether it is possible to infer whether an author will publish on a research activity before it has occurred. To develop and test our approach, we selected a civil nuclear activity for our case study. We constructed a state-action-state transition graph from publications of authors associated with the activity and the co-authors of their publications, using titles, abstracts, and author publication sequences. We then used inverse reinforcement learning to model the goal-directed behavior of authors in trajectories that terminate at selected goal states. Using a Bayesian formulation, we computed the probability that authors would reach each selected state from partially observed trajectories of their state transitions in their research topic space. The state with the highest probability was selected as the most probable goal state. Based on our results, we found that 60% of the times we can infer the correct goal state early; sometimes the inference is either delayed, or multiple states could be inferred as goal states. Overall, our results show that it is possible to perform early detection of research activities of authors in a nuclear technology area. Further research is necessary to establish a more accurate understanding of how topic modeling, topic space grid discretization, and the extent of overlap among trajectories of different goal states, affect the goal inference results. The methods developed in this work may be used to enhance data-driven methods for early detection of nuclear proliferation indicators.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Surface reconstructions and electronic structure of metallic delafossite thin films

The growing interest in the growth and study of thin films of low-dimensional metallic delafossites, with the general formula ABO2, is driven by their potential to exhibit electronic and magnetic characteristics that are not accessible in bulk systems. The layered structure of these compounds introduces unique surface states as well as electronic and structural reconstructions, making the investigation of their surface behavior pivotal to understanding their intrinsic electronic structure. In this work, we study the surface phenomena of epitaxially grown PtCoO2, PdCoO2, and PdCrO2 films, utilizing a combination of molecular-beam epitaxy and angle-resolved photoemission spectroscopy. Through precise control of surface termination and treatment, we discover a pronounced 3×3 surface reconstruction in PtCoO2 films and PdCoO2 films, alongside a 2 × 2 surface reconstruction observed in PdCrO2 films. These reconstructions have not been reported in prior studies of delafossites. Furthermore, our computational investigations demonstrate the BO2 surface’s relative stability compared to the A-terminated surface and the significant reduction in surface energy facilitated by the reconstruction of the A-terminated surface. These experimental and theoretical insights illuminate the complex surface dynamics in metallic delafossites, paving the way for future explorations of their distinctive properties in low-dimensional studies.

Materials Science↗

DOE OSTI2021

Probing the thermodynamics and kinetics of ethylene carbonate reduction at the electrode–electrolyte interface with molecular simulations

Understanding the formation of the solid-electrolyte interphase (SEI) in lithium-ion batteries (LIBs) is an ongoing area of research due to its high degree of complexity and the difficulties encountered by experimental studies. Herein, we investigate the initial stage of SEI growth, the reduction reaction of ethylene carbonate (EC), from both a thermodynamic and kinetic approach with theory and molecular simulation. We employed both the potential distribution theorem (PDT) and the SMD implicit solvent model to EC solvation for the estimation of reduction potentials of Li$^+$, EC, and Li$^+$-solvating EC (s-EC), as well as reduction rate constants of EC and s-EC. We find that solvation effects greatly influence these quantities of interest, particularly the Li$^+$/Li reference electrode potential in EC solvent. Further, we also compute the inner- and outer-sphere reorganization energies for both EC and s-EC at the interface of liquid EC and a hydroxyl-terminated graphite surface, where total reorganization energies are predicted to be 76.6 and 88.9 kcal/mol, respectively. With the computed reorganization energies, we estimate reduction rate constants across a range of overpotentials and show that EC has a larger electron transfer rate constant than s-EC at equilibrium, despite s-EC being more thermodynamically favorable. Furthermore, this manuscript demonstrates how ion solvation effects largely govern the prediction of reduction potentials and electron transfer rate constants at the electrode-electrolyte interface.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Design of an additively manufactured functionally graded material of 316 stainless steel and Ti-6Al-4V with Ni-20Cr, Cr, and V intermediate compositions

This study presents a method for designing a computationally informed gradient pathway to fabricate a functionally graded material (FGM) with terminal alloys of 316 stainless steel (SS316) and Ti-6Al-4V via directed energy deposition additive manufacturing with powder feedstock. In this work, the grading is accomplished through the introduction of intermediate elements and alloys (Ni-20Cr, Cr, and V) to avoid the brittle Fe-Ti intermetallic phases that form in the direct liquid phase joining of Ti-alloys and stainless steels. Using a combination of equilibrium calculations and Scheil-Gulliver simulations, a compositional pathway was designed to avoid deleterious phases. FGM samples were fabricated and experimentally characterized to determine the viability of the pathway. A change in phases from fcc to bcc was predicted to occur within the Ni-20Cr/Cr gradient region, and this was validated through experimental characterization. No detrimental phases (intermetallic, Laves, or σ phases) formed along the gradient path, demonstrating a successful computationally-informed design and fabrication of an FGM from SS316 to Ti-6Al-4V.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Leveraging Nitrogen Linkages in the Formation of a Porous Thorium–Organic Nanotube Suitable for Iodine Capture

We report the synthesis, characterization, and iodine capture application of a novel thorium–organic nanotube, TSN-626, [Th 6 O 4 (OH) 4 (C 6 H 4 NO 2 ) 7 (CHO 2 ) 5 (H 2 O) 3 ]·3H 2 O. The classification as a metal–organic nanotube (MONT) distinguishes it as a rare and reduced dimensionality subset of metal–organic frameworks (MOFs); the structure is additionally hallmarked by low node connectivity. TSN-626 is composed of hexameric thorium secondary building units and mixed O/N-donor isonicotinate ligands that demonstrate selective ditopicity, yielding both terminating and bridging moieties. Because hard Lewis acid tetravalent metals have a propensity to bind with electron donors of rival hardness (e.g., carboxylate groups), such Th–N coordination in a MOF is uncommon. However, the formation of key structural Th–N bonds in TSN-626 cap some of the square antiprismatic metal centers, a position usually occupied by terminal water ligands. TSN-626 was characterized by using complementary analytical and computational techniques: X-ray diffraction, vibrational spectroscopy, N 2 physisorption isotherms, and density functional theory. TSN-626 satisfies design aspects for the chemisorption of iodine. The synergy between accessibility through pores, vacancies at the metal–oxo nodes, and pendent N-donor sites allowed a saturated iodine loading of 955 mg g –1 by vapor methods. Furthermore, the crystallization of TSN-626 diversifies actinide–MOF linker selection to include soft electron donors, and these Th–N linkages can be leveraged for the investigation of metal-to-ligand bonding and unconventional topological expressions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Physical Compact Model for Three-Terminal SONOS Synaptic Circuit Element

A well-posed physics-based compact model for a three-terminal silicon–oxide–nitride–oxide–silicon (SONOS) synaptic circuit element is presented for use by neuromorphic circuit/system engineers. Based on technology computer aided design (TCAD) simulations of a SONOS device, the model contains a nonvolatile memristor with the state variable QM representing the memristor charge under the gate of the three-terminal element. By incorporating the exponential dependence of the memristance on QM and the applied bias V for the gate, the compact model agrees quantitatively with the results from TCAD simulations as well as experimental measurements for the drain current. The compact model is implemented through VerilogA in the circuit simulation package Cadence Spectre and reproduces the experimental training behavior for the source–drain conductance of a SONOS device after applying writing pulses ranging from –12 V to +11 V, with an accuracy higher than 90%.

42 ENGINEERING↗