Variational Deep Material Network: Enabling efficient aleatoric UQ in structure-property modeling
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Robertson, Andreas Euan [Sandia National Laborator↗
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Merger Response of Halo Anisotropy Properties
Anisotropy properties—halo spin, shape, position offset, velocity offset, and orientation—are an important family of dark matter halo properties that indicate the level of directional variation of the internal structures of halos. These properties reflect the dynamical state of halos, which in turn depends on the mass assembly history. In this work, we study the evolution of anisotropy properties in response to merger activity using the IllustrisTNG simulations. We find that the response trajectories of the anisotropy properties significantly deviate from secular evolution. These trajectories have the same qualitative features and timescales across a wide range of merger and host properties. We propose explanations for the behavior of these properties and connect their evolution to the relevant stages of merger dynamics. We measure the relevant dynamical timescales. We also explore the dependence of the strength of the response on time of merger, merger ratio, and mass of the main halo. These results provide insight into the physics of halo mergers and their effects on the statistical behavior of halo properties. This study paves the way toward a physical understanding of scaling relations, particularly to how systematics in their scatter are connected to the mass assembly histories of halos.
79 ASTRONOMY AND ASTROPHYSICS↗
Interlayer Triplet Energy Transfer in Dion-Jacobson 2D Perovskites Containing Naphthalene Diammonium Cations
The ability to tune the heterojunction energy level alignment in layered 2D perovskites is well documented, with both early works and more recent studies reporting interfacial triplet energy transfer in certain pairings of lead halide with small conjugated organic spacer cations. Such 2D perovskites are promising systems for sensitized molecular phosphorescence, which has varied applications in optoelectronics, solar energy conversion, and more. However, even for a given organic cation core, variations in the cation structure (e.g. location of the ammonium substituent, length of the alkyl linker, and valency of the cation) can dramatically alter the organic layer morphology and electronic coupling between the inorganic and organic layers. While interlayer triplet energy transfer has been studied in Ruddlesden-Popper 2D perovskites containing monovalent naphthalene cations, the photophysical properties of their Dion-Jacobson analogue, formed using divalent naphthalene cations, have not been reported. In this study, we examine interlayer energy transfer in a series of mixed-halide 2D perovskites formed by divalent naphthalene cations. We find that the sensitized phosphorescence in these compounds is dominated by naphthalene triplet excimer emission, but when the lead halide exciton level is tuned near resonance with the triplet level of naphthalene, emission from the naphthalene triplet monomer competes with triplet excimer formation. Interlayer energy transfer in these compounds is further supported by ultrafast transient absorption spectroscopy. Ultimately, the ability to gain control over interlayer interactions in layered 2D perovskites through cation design will help uncover new functions and applications for these materials.
2D↗
Discovery of Nb hydride precipitates in superconducting qubits
We report the first evidence of the formation of niobium hydrides within niobium films on silicon substrates in superconducting qubits fabricated at Rigetti Computing. We combine complementary techniques including room and cryogenic temperature atomic scale high-resolution and scanning transmission electron microscopy (HR-TEM and STEM), atomic force microscopy (AFM), and the time-of-flight secondary ion mass spectroscopy (TOF-SIMS) to reveal the existence of the niobium hydride precipitates directly in the Rigetti chip areas. Electron diffraction and high-resolution transmission electron microscopy (HR-TEM) analyses are performed at room and cryogenic temperatures (~106 K) on superconducting qubit niobium film areas, and reveal the formation of three types of Nb hydride domains with different crystalline orientations and atomic structures. There is also variation in their size and morphology from small (~5 nm) irregular shape domains within the Nb grains to large (~10-100 nm) Nb grains fully converted to niobium hydride. As niobium hydrides are non-superconducting and can easily change in size and location upon different cooldowns to cryogenic temperatures, our findings highlight a new previously unknown source of decoherence in superconducting qubits, contributing to both quasiparticle and two-level system (TLS) losses, and offering a potential explanation for qubit performance changes upon cooldowns. A pathway to mitigate the formation of the Nb hydrides for superconducting qubit applications is also discussed.
Lee, Jaeyel↗
Evaluating State Space Discovery by Persistent Cohomology in the Spatial Representation System
Persistent cohomology is a powerful technique for discovering topological structure in data. Strategies for its use in neuroscience are still undergoing development. We comprehensively and rigorously assess its performance in simulated neural recordings of the brain's spatial representation system. Grid, head direction, and conjunctive cell populations each span low-dimensional topological structures embedded in high-dimensional neural activity space. We evaluate the ability for persistent cohomology to discover these structures for different dataset dimensions, variations in spatial tuning, and forms of noise. We quantify its ability to decode simulated animal trajectories contained within these topological structures. We also identify regimes under which mixtures of populations form product topologies that can be detected. Our results reveal how dataset parameters affect the success of topological discovery and suggest principles for applying persistent cohomology, as well as persistent homology, to experimental neural recordings.
59 BASIC BIOLOGICAL SCIENCES↗
The Impact of Stand Age and Fertilization on the Soil Microbiome of Miscanthus × giganteus
Yield of the perennial grass Miscanthus × giganteus has shown an inconsistent and unpredictable response to nitrogen (N) fertilizer, yet fertilization underpins the crop’s environmental and economic sustainability. The interactions among soil microbial communities, N availability, and Miscanthus × giganteus and management may explain changes in plant productivity. In this study, soil samples from different stand ages of Miscanthus × giganteus in a replicated chronosequence field trial were used to investigate the effects of stand age and N fertilizer rates on microbial community structure. We hypothesized that there is a definable Miscanthus × giganteus soil microbiome and that this community varies significantly with stand age and fertilization. Our results showed that the main phyla in soil microbial communities, regardless of plant age, are similar but microbial community structures are significantly different. The variation in observed microbial communities generally decreases with older stand ages. The amount of N fertilizer applied also affected the microbial community structure associated with different aged Miscanthus × giganteus. Specifically, the relative abundance of Proteobacteria (Alphaproteobacteria and Gammaproteobacteria) and Acidobacteria (subgroup Gp1) increased shortly after fertilization and was more associated with younger Miscanthus × giganteus. Furthermore, our results show a significant relationship between bacterial α diversity and fertilization rates and that this response is also affected by stand age. Overall, our results emphasize linkages between microbial community structure, plant age, and fertilization in Miscanthus × giganteus.
59 BASIC BIOLOGICAL SCIENCES↗
Effects of Nonequilibrium Atomic Structure on Ionic Diffusivity in LLZO: A Classical and Machine Learning Molecular Dynamics Study
To improve the performance of electrochemical devices, it is essential to understand the effects of nonequilibrium motifs in solids, such as grain boundaries, amorphous phases, and highly strained regions, on atomic-scale transport and stability. Molecular dynamics simulations are used to explore the combined effect of far-from-equilibrium atomic structures and the choice of interatomic potential on ionic diffusivity predictions for Li 7 La 3 Zr 2 O 12 (LLZO), a promising solid electrolyte for all-solid-state batteries. Amorphization and high strain are considered using both classical Buckingham interatomic potentials and machine learning force fields. Here we find that both crystalline expansion and amorphization tend to slow diffusion, although the different physical encodings in the two potentials impact the properties in different ways. We trace these variations to a combination of structural and transport factors, the contributions of which are deconvoluted computationally. Graph-based analysis reveals that the variations for amorphous LLZO arise from the connectivity of diffusion pathways within the predicted structures, which generally correlates with diffusivity and is notably higher for structures generated by the machine learning force fields. Our study provides additional insight into the relationship between atomic structure and diffusivity in LLZO, while also highlighting the need for care in choosing and validating potentials to simulate far from equilibrium structures.
25 ENERGY STORAGE↗
Dissipative ground state preparation in ab initio electronic structure theory
Dissipative engineering is a powerful tool for quantum state preparation, and has drawn significant attention in quantum algorithms and quantum many-body physics in recent years. In this work, we introduce a novel approach using the Lindblad dynamics to efficiently prepare the ground state for general ab initio electronic structure problems on quantum computers, without variational parameters. These problems often involve Hamiltonians that lack geometric locality or sparsity structures, which we address by proposing two generic types of jump operators for the Lindblad dynamics. Type-I jump operators break the particle number symmetry and should be simulated in the Fock space. Type-II jump operators preserves the particle number symmetry and can be simulated more efficiently in the full configuration interaction space. For both types of jump operators, we prove that in a simplified Hartree-Fock framework, the spectral gap of our Lindbladian is lower bounded by a universal constant. For physical observables such as energy and reduced density matrices, the convergence rate of our Lindblad dynamics with Type-I jump operators remains universal, while the convergence rate with Type-II jump operators only depends on coarse grained information such as the number of orbitals and the number of electrons. To validate our approach, we employ a Monte Carlo trajectory-based algorithm for simulating the Lindblad dynamics for full ab initio Hamiltonians, demonstrating its effectiveness on molecular systems amenable to exact wavefunction treatment.
Quantum chemistry↗
Data‐driven variational method for discrepancy modeling: Dynamics with small‐strain nonlinear elasticity and viscoelasticity
Abstract The effective inclusion of a priori knowledge when embedding known data in physics‐based models of dynamical systems can ensure that the reconstructed model respects physical principles, while simultaneously improving the accuracy of the solution in the previously unseen regions of state space. This paper presents a physics‐constrained data‐driven discrepancy modeling method that variationally embeds known data in the modeling framework. The hierarchical structure of the method yields fine scale variational equations that facilitate the derivation of residuals which are comprised of the first‐principles theory and sensor‐based data from the dynamical system. The embedding of the sensor data via residual terms leads to discrepancy‐informed closure models that yield a method which is driven not only by boundary and initial conditions, but also by measurements that are taken at only a few observation points in the target system. Specifically, the data‐embedding term serves as residual‐based least‐squares loss function, thus retaining variational consistency. Another important relation arises from the interpretation of the stabilization tensor as a kernel function, thereby incorporating a priori knowledge of the problem and adding computational intelligence to the modeling framework. Numerical test cases show that when known data is taken into account, the data driven variational (DDV) method can correctly predict the system response in the presence of several types of discrepancies. Specifically, the damped solution and correct energy time histories are recovered by including known data in the undamped situation. Morlet wavelet analyses reveal that the surrogate problem with embedded data recovers the fundamental frequency band of the target system. The enhanced stability and accuracy of the DDV method is manifested via reconstructed displacement and velocity fields that yield time histories of strain and kinetic energies which match the target systems. The proposed DDV method also serves as a procedure for restoring eigenvalues and eigenvectors of a deficient dynamical system when known data is taken into account, as shown in the numerical test cases presented here.
Masud, Arif↗
Structural transitions of 4:1 methanol–ethanol mixture and silicone oil under high pressure
A 4:1 (volume ratio) methanol–ethanol (ME) mixture and silicone oil are two of the most widely used liquid pressure-transmitting media (PTM) in high-pressure studies. Their hydrostatic limits have been extensively studied using various methods; however, the evolution of the atomic structures associated with their emerging nonhydrostaticity remains unclear. Here, we monitor their structures as functions of pressure up to ∼30 GPa at room temperature using in situ high-pressure synchrotron x-ray diffraction (XRD), optical micro-Raman spectroscopy, and ruby fluorescence spectroscopy in a diamond anvil cell. No crystallization is observed for either PTM. The pressure dependence of the principal diffraction peak position and width indicates the existence of a glass transition in the 4:1 ME mixture at ∼12 GPa and in the silicone oil at ∼3 GPa, beyond which a pressure gradient emerges and grows quickly with pressure. There may be another liquid-to-liquid transition in the 4:1 ME mixture at ∼5 GPa and two more glass-to-glass transitions in the silicone oil at ∼10 GPa and ∼16 GPa. By contrast, Raman signals only show peak weakening and broadening for typical structural disordering, and Raman spectroscopy seems to be less sensitive than XRD in catching these structural transitions related to hydrostaticity variations in both PTM. These results uncover rich pressure-induced transitions in the two PTM and clarify their effects on hydrostaticity with direct structural evidence. The high-pressure XRD and Raman data on the two PTM obtained in this work could also be helpful in distinguishing between signals from samples and those from PTM in future high-pressure experiments.
Chen, Xiehang (ORCID:0000000168541506)↗
Seasonality regulates the structure and biogeochemical impact of ectomycorrhizal fungal communities across environmentally divergent neotropical dry forests
Abstract Ectomycorrhizal (ECM) symbioses support forest functioning globally, yet both the structure and function of ECM fungal communities in seasonally dry neotropical forests (SDTFs), known for extreme heterogeneity in vegetation and edaphic properties, remain under characterized. Here, we evaluated the relative influences of seasonal versus spatial variation in ECM fungal community structure in soils from four environmentally divergent SDTFs. We also assessed the importance of biotic and abiotic drivers of SDTF ECM fungal community structure at regional scales, as well as ECM impacts on soil carbon (C) and nitrogen (N) cycling. ECM fungal frequency, relative abundance and richness all increased in the wet season, but spatial rather than seasonal effects explained more variation in community composition. Across the four SDTFs investigated, differences in tree communities drove ECM fungal community turnover more than geographic distances, site abiotic conditions or soil chemistry. Although soil moisture and ECM tree basal area were stronger predictors of soil biogeochemistry, incorporating ECM fungal community composition and relative abundance added explanatory power to models of soil C and N cycling in the wet season. Synthesis : Our results highlight the importance of seasonality and plant community composition in shaping different aspects of SDTF ECM fungal community structure and diversity as well as the potential for both the plant and fungal components of ECM symbioses to impact soil functioning across heterogenous SDTFs. Furthermore, our findings suggest that alterations in SDTF plant community composition due to climate or land‐use change will have important consequences for ECM fungal diversity and associated effects on soil biogeochemical cycling.
54 ENVIRONMENTAL SCIENCES↗
Describing the Influence of Ball-milling on the Amorphization of Flubendazole Using the PDF and RMC Methods with X-ray Powder Diffraction Data
Flubendazole (FBZ) is a poorly water-soluble drug, and different methodologies have been proposed to improve its oral bioavailability. Obtaining the amorphous drug phase is an alternative to improve its water solubility. Several techniques for drug amorphization, such as spray drying, lyophilization, melt quenching, solvent-evaporation, and ball milling, can yield various types of structural disorder and possibly render variations in physicochemical properties. Herein, we focus on evaluating the influence of the ball-milling process on the amorphization of FBZ. The characterization of the average global and local structures before, during, and after the milling process is described by sequential Rietveld refinements, pair distribution function analysis, and the Reverse Monte Carlo method. In conclusion, we show that preserving the local structure (nearest molecules) can be responsible for avoiding the fast structure recrystallization commonly observed when using the solvent-evaporation process for the studied drug.
60 APPLIED LIFE SCIENCES↗
Unsupervised learning approaches to characterizing heterogeneous samples using X-ray single-particle imaging
One of the outstanding analytical problems in X-ray single-particle imaging (SPI) is the classification of structural heterogeneity, which is especially difficult given the low signal-to-noise ratios of individual patterns and the fact that even identical objects can yield patterns that vary greatly when orientation is taken into consideration. Proposed here are two methods which explicitly account for this orientation-induced variation and can robustly determine the structural landscape of a sample ensemble. The first, termed common-line principal component analysis (PCA), provides a rough classification which is essentially parameter free and can be run automatically on any SPI dataset. The second method, utilizing variation auto-encoders (VAEs), can generate 3D structures of the objects at any point in the structural landscape. Both these methods are implemented in combination with the noise-tolerant expand–maximize–compress (EMC) algorithm and its utility is demonstrated by applying it to an experimental dataset from gold nanoparticles with only a few thousand photons per pattern. Both discrete structural classes and continuous deformations are recovered. These developments diverge from previous approaches of extracting reproducible subsets of patterns from a dataset and open up the possibility of moving beyond the study of homogeneous sample sets to addressing open questions on topics such as nanocrystal growth and dynamics, as well as phase transitions which have not been externally triggered.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
ABC -Auxetics: An Implicit Design Approach for Negative Poisson's Ratio Materials
A novel methodology is introduced for designing auxetic (negative Poisson's ratio) structures based on topological principles and is demonstrated by investigating a new class of auxetics based on two-dimensional (2D) textile weave patterns. Conventional methodology for designing auxetic materials typically involves determining a single deformable block (a unit cell) of material whose shape results in auxetic behavior. Consequently, patterning such a unit cell in a 2D (or 3D) domain results in a larger structure that exhibits overall auxetic behavior. Such an approach naturally relies on some prior intuition and experience regarding which unit cells may be auxetic. Second, tuning the properties of the resulting structures is typically limited to parametric variations of the geometry of a specific type of unit cell. Thus, most of the currently known auxetic structures belong to a selected few classes of unit cell geometries that are explicitly defined in accordance with a specified topological (i.e., grid structure). Herein, a new class of auxetic structures is demonstrated that, while periodic, can be generated implicitly, i.e., without reference to a specific unit cell design. The approach leverages weave-based parameters (A–B–C), resulting in a rich design space for auxetics that is previously unexplored.
36 MATERIALS SCIENCE↗
Pair-Variational Autoencoders for Linking and Cross-Reconstruction of Characterization Data from Complementary Structural Characterization Techniques
Not Available
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Molecular basis of C-S bond cleavage in the glycyl radical enzyme isethionate sulfite-lyase
Desulfonation of isethionate by the bacterial glycyl radical enzyme (GRE) isethionate sulfite-lyase (IslA) generates sulfite, a substrate for respiration that in turn produces the disease-associated metabolite hydrogen sulfide. Here, we present a 2.7 Å resolution X-ray structure of wild-type IslA from Bilophila wadsworthia with isethionate bound. In comparison with other GREs, alternate positioning of the active site β strands allows for distinct residue positions to contribute to substrate binding. These structural differences, combined with sequence variations, create a highly tailored active site for the binding of the negatively charged isethionate substrate. Through the kinetic analysis of 14 IslA variants and computational analyses, we probe the mechanism by which radical chemistry is used for C-S bond cleavage. This work further elucidates the structural basis of chemistry within the GRE superfamily and will inform structure-based inhibitor design of IsIA and thus of microbial hydrogen sulfide production.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Do Genetic Drift and Gene Flow Affect the Geographic Distribution of Female Plants in Gynodioecious Lobelia siphilitica?
Variation in population sex ratio is particularly pronounced in gynodioecious angiosperms. Extremely high female frequencies in gynodioecious populations cannot be readily explained by selective forces alone. To assess the contributions of drift and gene flow to extreme sex-ratio variation, we documented sex ratio and population size in 92 populations of Lobelia siphilitica across its range and genotyped plants using plastid and nuclear genetic markers. Similarity in spatial patterns of genetic and demographic variables may suggest that drift and/or gene flow have contributed to population sex-ratio variation in L. siphilitica. We found strong spatial structuring of extremely high female frequencies: populations with >50% female plants are restricted to the south–central portion of the range. However, we did not detect any spatial structuring in population size nor metrics of genetic diversity, suggesting that extreme variation in female frequency is not strongly affected by drift or gene flow. Extreme sex-ratio variation is frequently observed in gynodioecious plants, but its causes are difficult to identify. Further investigation into mechanisms that create or maintain the spatial structure of sex ratios in gynodioecious species will provide much needed insight.
54 ENVIRONMENTAL SCIENCES↗
Elucidating the Influence of Residual Polymer and Gas Environment on the Electronic Structure of a Graphene Layer Using in Situ APXPS
We use in situ ambient pressure X-ray photoelectron spectroscopy (APXPS) and ultraviolet photoelectron spectroscopy (UPS) to develop an effective method for studying changes in the graphene (Gr) electronic structure according to certain circumstance. The amount of polymethyl methacrylate (PMMA) residual polymer (RP), inevitably generated during the Gr transfer process, is significantly reduced from Gr surface by thermal annealing. This processed Gr is then sequentially exposed to specific gas environments (Ar, N 2 , O 2 , and CO 2 ), and APXPS or UPS is carried out to investigate the variations in the Gr electronic structure including the work function. When the amount of PMMA RP on Gr is reduced, the position of the main carbon peak shifts by more than 0.4 eV to a higher binding energy (in XPS spectra), and the secondary electron cutoff moves by about 0.2 eV to a lower binding energy (in UPS spectra). These changes are generally caused by a decrease in the Gr work function. On the other hand, exposure to the gas environments at different temperatures that we investigated did not produce significant changes in the work function and chemical states of Gr. These results confirm that the material in contact with Gr should be considered to achieve the desired Gr performance in electronics.
36 MATERIALS SCIENCE↗