Statistical learning of governing equations of dynamics from in-situ electron microscopy imaging data
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36 MATERIALS SCIENCE↗
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Microstructure reconstruction of battery polymer separators by fusing 2D and 3D image data for transport property analysis
A new approach to generate high-fidelity 3D microstructure reconstructions by leveraging resolution and sample volume characteristics from 2D and 3D microscopy methods is presented here. This approach is employed to model the microstructure of a highly orthotropic polypropylene separator used in lithium-ion batteries, which have challenging multi-scale features of fibrils (<100 nm) and lamellae (>100 nm) to resolve in 3D. Phase contrast nano X-ray computed tomographic data are used to reconstruct the lamellae phase, while 2D scanning electron microscopy data are used to characterize sub-100 nm microstructure features such as the thin fibrils that are beyond the effective resolution of X-ray computed tomography. Fibril geometries are reconstructed stochastically based on the 2D higher resolution data, and integrated with the lamellae geometries in 3D space. Transport property analyses are performed to investigate the bias of microstructure models without considering the fibrils. A sensitivity study is also conducted to facilitate understanding of the relationship between microstructure characteristics and transport properties.
25 ENERGY STORAGE↗
Causal discovery from data assisted by large language models
Knowledge-driven discovery of novel materials necessitates the development of causal models for property emergence. While in the classical physical paradigm, the causal relationships are deduced based on physical principles or via experiment, the rapid accumulation of observational data necessitates learning causal relationships between dissimilar aspects of material structure and functionalities based on observations. For this, it is essential to integrate experimental data with prior domain knowledge. Here, we demonstrate this approach by combining high-resolution scanning transmission electron microscopy data with insights derived from large language models (LLMs). By applying ChatGPT to domain-specific literature, such as arXiv papers on ferroelectrics, and combining the obtained information with data-driven causal discovery, we construct adjacency matrices for directed acyclic graphs that map the causal relationships between structural, chemical, and polarization degrees of freedom in Sm-doped BiFeO 3 . This approach enables us to hypothesize how synthesis conditions influence material properties and guides experimental validation. Furthermore, the ultimate objective of this work is to develop a unified framework that integrates LLM-driven literature analysis with data-driven discovery, facilitating the precise engineering of ferroelectric materials by establishing clear connections between synthesis conditions and their resulting material properties.
Causal inference↗
A multi-method characterization of natural terrestrial birnessites
Abstract not provided
Ling, Florence T.↗
CryoFold: Determining protein structures and data-guided ensembles from cryo-EM density maps
Cryoelectron microscopy requires molecular modeling for refinement of structures. Ensemble models arrive at low free-energy molecular structures, but are computationally expensive and limited to resolving only small proteins. Here, we introduce CryoFold, a pipeline of molecular dynamics simulations that determines ensembles of protein structures by integrating density data of varying sparsity at 3–5 Å resolution with sequence information and coarse-grained topological knowledge of the protein folds. We present six examples, folding proteins between 72 and 2,000 residues, including large membrane and multi-domain systems, and results from two Electron Microscopy Data Bank (EMDB) competitions. Driven by data from a single state, CryoFold discovers ensembles of common low-energy models together with rare low-probability structures that capture the equilibrium distribution of proteins constrained by the density maps. Many of these conformations are experimentally validated and functionally relevant. We arrive at a set of best practices for data-guided protein folding that are controlled using a Python graphical user interface (GUI).
59 BASIC BIOLOGICAL SCIENCES↗
Nanoscale imaging of quantum dot dimers using time-resolved super-resolution microscopy combined with scanning electron microscopy
Abstract Time-resolved super-resolution microscopy was used in conjunction with scanning electron microscopy to image individual colloidal CdSe/CdS semiconductor quantum dots (QD) and QD dimers. The photoluminescence (PL) lifetimes, intensities, and structural parameters were acquired with nanometer scale spatial resolution and sub-nanosecond time resolution. The combination of these two techniques was more powerful than either alone, enabling us to resolve the PL properties of individual QDs within QD dimers as they blinked on and off, measure interparticle distances, and identify QDs that may be participating in energy transfer. The localization precision of our optical imaging technique was ∼3 nm, low enough that the emission from individual QDs within the dimers could be spatially resolved. While the majority of QDs within dimers acted as independent emitters, at least one pair of QDs in our study exhibited lifetime and intensity behaviors consistent with resonance energy transfer from a shorter lifetime and lower intensity donor QD to a longer lifetime and higher intensity acceptor QD. For this case, we demonstrate how the combined super-resolution optical imaging and scanning electron microscopy data can be used to characterize the energy transfer rate.
36 MATERIALS SCIENCE↗
Quantitative Structure Determination from Experimental Four-Dimensional Scanning Transmission Electron Microscopy via the Scattering Matrix
Considerable inroads have recently been made on algorithms to determine the sample potential from four-dimensional scanning transmission electron microscopy data from thick samples where multiple scattering cannot be neglected. This paper further develops the scattering matrix approach to such structure determination. Through simulation, we demonstrate how this approach can be modified to better handle partial spatial coherence, unknown probe defocus, and information from the dark field region. By combining these developments we reconstruct the electrostatic potential of a monolithic SrTiO 3 crystal showing good quantitative agreement with the expected structure.
4D STEM↗
Unveiling Charge Transport and Degradation Mechanisms of Aqueous Zn|α-MoO 3 Batteries in Conventional Concentration and Water-in-Salt Electrolytes: A Multi-Modal In Situ and Operando Study
Herein charge storage and transport properties are elucidated and cell degradation mechanisms of rechargeable aqueous Zn|alpha-MoO 3 batteries in three electrolyte systems (3 m ZnSO 4 , 3 m ZnCl 2 , and 30 m ZnCl 2 [12.5 m] water-in-salt (WIS)) are distinguished by a combination of in situ X-ray diffraction (XRD), in situ X-ray absorption spectroscopy (XAS), operando optoelectrochemistry, and operando energy dispersive X-ray diffraction (EDXRD). In conventional concentration 3 m electrolytes, in situ XRD and XAS, as well as ex situ scanning transmission electron microscopy data collectively support Zn 2+ as the primary charge carrier. In addition, these systems are susceptible to cathode dissolution, Zn corrosion coupled with the hydrogen evolution reaction, and the resultant formation of basic zinc salt phases. The multi-modal in situ and operando experimental analyses validate facile H+ intercalation and extraction in concentrated 30 m ZnCl 2 WIS electrolyte. Via operando EDXRD, reaction front and charge transport limitation during discharge and charge in the viscous WIS electrolyte are spatially tracked. Here this work provides new insight into the stability and degradation mechanisms of aqueous zinc batteries during static storage and upon dynamic cycling, and highlights the utility of in situ and operando techniques in understanding the superior stability of WIS electrolytes.
25 ENERGY STORAGE↗
Likelihood Maximization and Moment Matching in Low SNR Gaussian Mixture Models
We derive an asymptotic expansion for the log-likelihood of Gaussian mixture models (GMMs) with equal covariance matrices in the low signal-to-noise regime. The expansion reveals an intimate connection between two types of algorithms for parameter estimation: the method of moments and likelihood optimizing algorithms such as Expectation-Maximization (EM). We show that likelihood optimization in the low SNR regime reduces to a sequence of least squares optimization problems that match the moments of the estimate to the ground truth moments one by one. This connection is a stepping stone towards the analysis of EM and maximum likelihood estimation in a wide range of models. A motivating application for the study of low SNR mixture models is cryo-electron microscopy data, which can be modeled as a GMM with algebraic constraints imposed on the mixture centers. We discuss the application of our expansion to algebraically constrained GMMs, among other example models of interest. © 2022 The Authors. Communications on Pure and Applied Mathematics published by Wiley Periodicals LLC.
97 MATHEMATICS AND COMPUTING↗
The Phenix‐AlphaFold webservice: Enabling AlphaFold predictions for use in Phenix
Abstract Advances in machine learning have enabled sufficiently accurate predictions of protein structure to be used in macromolecular structure determination with crystallography and cryo‐electron microscopy data. The Phenix software suite has AlphaFold predictions integrated into an automated pipeline that can start with an amino acid sequence and data, and automatically perform model‐building and refinement to return a protein model fitted into the data. Due to the steep technical requirements of running AlphaFold efficiently, we have implemented a Phenix‐AlphaFold webservice that enables all Phenix users to run AlphaFold predictions remotely from the Phenix GUI starting with the official 1.21 release. This webservice will be improved based on how it is used by the research community and the future research directions for Phenix.
Poon, Billy K.↗
Correlation between cation order/disorder and the electrocaloric effect in the MLCCs of complex perovskite ferroelectrics
The physical properties (dielectric, ferroelectric, piezoelectric, etc.) of complex perovskite ferroelectrics depend on the degree of order/disorder and the scale of the ordered domains. In this study, the electrocaloric (EC) properties of three representative complex perovskite ferroelectrics, Pb(Mg 1/3 Nb 2/3 )O 3 -8PbTiO 3 (PMN-8PT), 1mol% Sm-doped Pb(Mg 1/3 Nb 2/3 )O 3 -8PbTiO 3 (1S-PMN-8PT) and Pb(Sc 1/2 Ta 1/2 )O 3 (PST) are evaluated. Multi-layer ceramic capacitors (MLCCs) with identical structural configurations were fabricated for these three compounds, and their EC properties were characterized by direct measurement using a thermocouple. The EC temperature change of PMN-8PT, 1S-PMN-8PT and PST MLCCs under 20 V μm –1 at room temperature were found to be 1.42 K, 1.54 K, and 3.10 K, respectively. X-ray diffraction and high-resolution transmission electron microscopy data suggests that the high EC performance of PST is related to the ordering of B-site cations (Sc 3+ and Ta 5+ ) with the ordering parameter S = 0.82 and a long coherence length of ~100 nm, such that the sample transitioned from a relaxor ferroelectric to a normal ferroelectric. Furthermore, these results provide pathway towards design of high performance EC materials required for solid state refrigeration and air-conditioning technologies.
36 MATERIALS SCIENCE↗
Electron microscopy characterization of the fuel-cladding interaction in medium burnup annular fast reactor MOX
In this work, we present electron microscopy data focused on the fuel-cladding interaction layer in annular fast reactor MOX with HT-9 cladding at medium burnup. In agreement with previous literature data, the volatile fission products Cs, Te and I have migrated radially into the extreme fuel periphery and partially interacted with the cladding. The accumulation of Cs has occurred in the outermost rim of the fuel pellet, where grain recrystallization has also been observed. Significant amounts of Pd have been found in the interaction zone, particularly in the sample taken from the upper half of the fissile column where the cladding temperatures are higher. At this axial location, Cr has been enriched at the cladding inner surface and diffused into the fuel. Furthermore, chromium remains mainly in metallic form, but locally formed oxides. The fission products Cs, Te and I are found, with variable composition, in form of nanocrystalline regions dispersed in the metallic Cr-rich layer. The morphology and chemical characteristics of the layer suggest a non-oxidative corrosion mechanism as principal cladding degradation phenomenon occurring in this sample, with local onset of Cr oxidation within the nanocrystalline precipitates.
36 MATERIALS SCIENCE↗
EXSCLAIM!: Harnessing materials science literature for self-labeled microscopy datasets
This work introduces the EXSCLAIM! toolkit for the automatic extraction, separation, and caption-based natural language annotation of images from scientific literature. EXSCLAIM! is used to show how rule-based natural language processing and image recognition can be leveraged to construct an electron microscopy data set containing thousands of keyword-annotated nanostructure images. Moreover, it is demonstrated how a combination of statistical topic modeling and semantic word similarity comparisons can be used to increase the number and variety of keyword annotations on top of the standard annotations from EXSCLAIM! With large-scale imaging datasets constructed from scientific literature, users are well positioned to train neural networks for classification and recognition tasks specific to microscopy-tasks often otherwise inhibited by a lack of sufficient annotated training data.
36 MATERIALS SCIENCE↗
Nanoscale Mg–B via Surfactant Ball Milling of MgB 2 : Morphology, Composition, and Improved Hydrogen Storage Properties
Metal borides have attracted the attention of researchers due to their useful physical properties and unique ability to form high hydrogen-capacity metal borohydrides. We demonstrate improved hydrogen storage properties of a nanoscale Mg–B material made by surfactant ball milling MgB 2 in a mixture of heptane, oleic acid, and oleylamine. Transmission electron microscopy data show that Mg–B nanoplatelets are produced with sizes ranging from 5 to 50 nm, which agglomerate upon ethanol washing to produce an agglomerated nanoscale Mg–B material of micron-sized particles with some surfactant still remaining. X-ray diffraction measurements reveal a two-component material where 32% of the solid is a strained crystalline solid maintaining the hexagonal structure with the remainder being amorphous. Fourier transform infrared shows that the oleate binds in a “bridge-bonding” fashion preferentially to magnesium rather than boron, which is confirmed by density functional theory calculations. The Mg–B nanoscale material is deficient in boron relative to bulk MgB 2 with a Mg–B ratio of ~1:0.75. The nanoscale MgB 0.75 material has a disrupted B–B ring network as indicated by X-ray absorption measurements. Hydrogenation experiments at 700 bar and 280 °C show that it partially hydrogenates at temperatures 100 °C below the threshold for bulk MgB 2 hydrogenation. In addition, upon heating to 200 °C, the H–H bond-breaking ability increases ~10-fold according to hydrogen–deuterium exchange experiments due to desorption of oleate at the surface. This behavior would make the nanoscale Mg–B material useful as an additive where rapid H–H bond breaking is needed.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Bond-centric modular design of protein assemblies
Directional interactions that generate regular coordination geometries are a powerful means of guiding molecular and colloidal self-assembly, but implementing such high-level interactions with proteins remains challenging due to their complex shapes and intricate interface properties. Here we describe a modular approach to protein nanomaterial design inspired by the rich chemical diversity that can be generated from the small number of atomic valencies. We design protein building blocks using deep learning-based generative tools, incorporating regular coordination geometries and tailorable bonding interactions that enable the assembly of diverse closed and open architectures guided by simple geometric principles. Experimental characterization confirms the successful formation of more than 20 multicomponent polyhedral protein cages, two-dimensional arrays and three-dimensional protein lattices, with a high (10%–50%) success rate and electron microscopy data closely matching the corresponding design models. Due to modularity, individual building blocks can assemble with different partners to generate distinct regular assemblies, resulting in an economy of parts and enabling the construction of reconfigurable networks for designer nanomaterials.
Biomaterials – proteins↗
Direct observation of magnetic ordering induced via systematic lattice disorder in artificial rhombus spin ices
It is critical to understand the effect of lattice geometry on the order parameter of a condensed matter system, as it controls phase transitions in such systems. Artificial spin ices (ASIs) are two-dimensional lattices of Ising-like nanomagnets that provide an opportunity to explore such phenomena by lithographically controlling the lattice geometry to observe its influence on magnetic ordering and frustration effects. Here we report a systematic approach to studying the effects of disorder in rhombus ASIs generated from combinations of five vertex motifs. We investigate four geometries characterized by a geometric order parameter, with symmetries ranging from periodic to quasiperiodic to random. Lorentz transmission electron microscopy data indicates magnetic domain behavior depends on chains of strongly-coupled islands in the periodic and sixfold-twinned lattices, while the behavior of the disordered lattice is dominated by vertex motifs with large configurational degeneracy. Utilizing micromagnetic simulations, a quantitative analysis of the lattice energetics showed that the experimental rotationally-demagnetized state of the disordered ASI was closer in energy to the idealized ground state compared to other periodic and twinned ASIs. Finally, our work provides a unique pathway for using degeneracy, magnetic frustration, and order to control the magnetization behavior of designer disordered systems.
36 MATERIALS SCIENCE↗
Understanding curvature effects on the magnetization reversal of patterned permalloy Archimedean spirals
Geometric curvature in magnetic systems can induce several unique magnetic interactions, the most notable of which are the curvature induced magnetic anisotropy and the curvature introduced Dzyaloshinski-Moria (DM) like interaction. Of the two, the DM-like interaction is particularly interesting as it provides a unique way to control domain wall motion, which is particularly relevant to the field of spintronics. In this work, we study the effect of geometric curvature on magnetization reversal in Permalloy strips patterned into Archimedean spirals with varying widths. Specifically, we simulated the magnetization reversal of Permalloy strips for several widths ranging from microscale to nanoscale to determine a lengthscale at which the curvature effects are strong. Simulations showed that in the microscale spirals, magnetization reversal primarily occurs through the appearance of magnetization ripple in which domains with reversed magnetization nucleate. On the other hand, the nanoscale spirals showed that reversal primarily proceeded through the motion of domain walls into the arms of the spiral. Our experimental in situ Lorentz transmission electron microscopy data of the microscale spirals, however, showed that magnetization reversal occurred with both mechanisms. At the nanoscale, the effect of local variation of curvature leads to hysteresis loops with stepped behavior. This behavior is characteristic of the curvature induced DM-like interaction affecting domain wall motion in the arms of the nanoscale spirals.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Unsupervised learning of ferroic variants from atomically resolved STEM images
An approach for the analysis of atomically resolved scanning transmission electron microscopy data with multiple ferroic variants in the presence of imaging non-idealities and chemical variabilities based on a rotationally invariant variational autoencoder (rVAE) is presented. We show that an optimal local descriptor for the analysis is a sub-image centered at specific atomic units, since materials and microscope distortions preclude the use of an ideal lattice as a reference point. The applicability of unsupervised clustering and dimensionality reduction methods is explored and is shown to produce clusters dominated by chemical and microscope effects, with a large number of classes required to establish the presence of rotational variants. Comparatively, the rVAE allows extraction of the angle corresponding to the orientation of ferroic variants explicitly, enabling straightforward identification of the ferroic variants as regions with constant or smoothly changing latent variables and sharp orientational changes. This approach allows further exploration of the chemical variability by separating the rotational degrees of freedom via rVAE and searching for remaining variability in the system. The code used in this article is available at https://github.com/saimani5/ferroelectric_domains_rVAE.
30 DIRECT ENERGY CONVERSION↗