Accelerated Exploration of Empty Material Compositional Space: Mg–Fe–B Ternary Metal Borides
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Zhang, Zhen↗
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Towards libraries of different ultrasmall amorphous silica cage topologies
Cage topology controlled at the nanometer length scale is expected to enable original functionalities, e.g., in catalysis and therapeutics. Cages are fundamental structural motifs of clathrates and their topological duals, Frank-Kasper phases, and are constituents of mesoporous silica frameworks. However, with one exception, they have not been synthesized as discrete particles. By varying reactant ratios of surfactant, oil, and silane, we report the discovery of 5(12)6(2) and 5(12)6(8) amorphous silica polyhedra, each coexisting with the previously identified 5(12) cage, using combined cryo-transmission electron microscopy (cryo-TEM) and single-particle reconstruction (SPR). Notably, the 5(12)6(8) polyhedron represents a topology not previously observed in mesoporous silica frameworks. Control over structural features is demonstrated, and insights into cage formation mechanisms are provided. Structural outcomes are summarized in a ternary morphology diagram alongside prior results, bridging serendipitous discovery and intentional design of silica cages displaying a level of control over silica polymerization rivaling nature.
Jang, Dong June [Department of Materials Science a↗
Synergistic Evolution of Segmental Motion and Cooperative Relaxation within the Amorphous Phase of Organic Mixed Ionic-Electronic Conductors
Organic mixed ionic-electronic conductors (OMIECs) facilitate a variety of electrochemical processes and feature a heterogeneous microstructure composed of both crystalline and amorphous phases. However, structural evolution in amorphous regions during electrochemical doping remains poorly understood, limiting our understanding of mixed conduction mechanisms. Here, in this work, we develop operando chip calorimetry to probe amorphous phase evolution in poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) under swelling and electrochemical (de)doping. Our results reveal that amorphous regions providing ionic transport pathways and those within electronic transport channels undergo heterogeneous, yet synergistic, evolution during electrochemical modulation. The cooperative interplay between segmental motion and relaxation maintains ionic conductivity and intergrain electronic transport upon electrowetting and doping, while facilitating efficient ion hopping and adaptable chain conformations during dedoping. Such synergies are more pronounced in loose structures featuring a fibrillar morphology, which exhibit lower glass transition temperatures (T g ) and higher fragility (m). High-throughput robotic screening further establishes a strong correlation between elevated m/T g ratios and enhanced mixed conduction. These findings elucidate the role of the amorphous phase in the synthesis of OMIECs and underscore the potential of operando chip calorimetry in uncovering structure–property relationships in electroactive polymers.
amorphous phase↗
New Phosphoranylideneketenes and Phosphoranylidenethioketenes‐Synthesis, Structure, Spectroscopy, and Lewis Base Properties
Posphoranylideneketene Ph 3 PCCO and its sulfur analogue, Ph 3 PCCS, are cumulated ylides that exhibit diverse reactivity and distinct structural features. Herein, we present the first systematic study of substituent effects on the structures and bonding of a series of aryl- and alkyl-substituted phosphoranylideneketenes, R 3 PCCO [R = Et, cyclohexyl, NMe 2 , 4-Me 2 N-C 6 H 4 , 4-MeO-C 6 H 4 , 4-CF 3 -C 6 H 4 , 3,5-(CF 3 ) 2 -C 6 H 3 ], prepared from the corresponding phosphonium salts, [R 3 PCH 2 COOEt]X (X = Br, I) and two equivalents of Na[N(SiMe 3 ) 2 ]. Multinuclear NMR and IR spectroscopy, supported by single-crystal X-ray diffraction, revealed that the bond strength within the central P═C═C═O fragment increases with the electron-deficiency of the aryl substituents at phosphorus. The corresponding phosphoranylidenethioketenes, R 3 PCCS (R = Et, cyclohexyl, NMe 2 , 4-Me 2 N-C 6 H 4 , 4-MeO-C 6 H 4 , 4-CF 3 -C 6 H 4 ), were obtained by reactions of R 3 PCCO with CS 2 . Here, the rate of conversion of R 3 PCCO into R 3 PCCS decreases with increasing electron deficiency at phosphorus. Both R 3 PCCO and R 3 PCCS act as relatively weak ambidentate Lewis donors, yet they form stable acid-base adducts with strong Lewis acids such as B(C 6 F 5 ) 3 and Al(C 6 F 5 ) 3 .
Lewis acid-base adduct↗
Decoding substrate specificity determining factors in glycosyltransferase-B enzymes – insights from machine learning models
Substrate specificity is an essential characteristic of any enzyme's function and an understanding of the factors that determine this specificity is crucial for enzyme engineering. Unlike the structure of an enzyme which is directly impacted by its sequence, substrate specificity as an enzyme attribute involves a rather indirect relationship with sequence as it also depends on structural aspects that dictate substrate accessibility and active site dynamics. In this study, we explore the performance of classifier-based machine learning models trained on curated sequence and structural data for a class of glycosyltransferases (GTs), namely GT-Bs, to understand their substrate specificity determining factors. GTs enable the transfer of sugar moieties to other biomolecules such as oligosaccharides or proteins and are found in all kingdoms of life. In plants, GTs participate in the biosynthesis of plant cell wall biopolymers (e.g.: hemicelluloses and pectins) and are an integral part of the enzymatic machinery that enables the storage of carbon and energy as plant biomass. To elucidate the substrate specificity of uncharacterized GT-Bs, we constructed multi-label machine learning models (Support Vector Classifier, K-Nearest Neighbors, Gaussian Naïve-Bayes, Random Forest) that incorporate both sequence and structural features. These models achieve good predictive accuracies on test datasets. However, despite our use of structural information, we highlight that there is further scope for improvement in training these models to draw interpretable relationships between sequence, structure and substrate specificity determining motifs in GT-Bs.
97 MATHEMATICS AND COMPUTING↗
Allosteric prediction via convolutional neural networks and protein structural and dynamical features
Allostery is the phenomenon whereby a binding event or covalent modification at one site in a protein modulates function at a distal site, thus changing a protein’s functional state. As such, it is a ubiquitous aspect of protein functional regulation. Computationally predicting allosteric states is important as part of the broader challenge of functional annotation, but it also has practical implications for drug development, as targeting an allosteric site often affords greater specificity compared with targeting an orthosteric site. This study introduces a machine learning approach to predict the allosteric functional state using the small G-protein KRas as the model system, due to its implication in many types of cancer and being well studied as a result with many x-ray crystallographic structures of KRas available with different mutations and ligands bound. Using structural and dynamical features that can be cast as images, namely interatomic distances, contact maps, covariance, and mutual information, supervised learning was performed using convolutional neural networks. Two pretrained convolutional neural network architectures, GoogLeNet and ResNet18, were fine-tuned to classify KRas into active or inactive states based on these features. Across training regimes, atomic contact maps emerged as the most effective structural feature, whereas linearized mutual information outperformed covariance in capturing dynamical correlations relevant to allostery. Models achieved significant validation accuracy, with atomic contact maps yielding up to 90% accuracy. In conclusion, the findings suggest that integrating global structural rearrangements and correlated motion patterns with deep learning can reliably predict protein allosteric states, offering a promising framework for understanding allosteric regulation and developing targeted therapeutics.
Rajeshwar T., Rajitha [Oak Ridge National Laborato↗
Metal–Organic Frameworks at the Edge of Stability: Mediating Node Distortion to Access Metastable Nanoparticle Polymorphs
Metal-organic frameworks (MOFs) are emerging as unconventional precursors for nanoparticle synthesis, with potential to leverage their tunable structures and chemistry to achieve nanomaterials with structures and compositions inaccessible via traditional synthetic routes. Here we use in situ synchrotron X-ray diffraction and pair distribution function (PDF) measurements to investigate how the dynamic structure of MOFs at the edge of stability influences their transformation into different metastable polymorphs. Our study reveals that the local structural features of metal-oxo MOF nodes at elevated temperatures are linked to the resulting nanoparticle structures formed under mild conditions. Focusing on the titanium-based MOF MIL-125, we demonstrate that manipulating the chemical environment to facilitate transformation of the Ti8 node geometry promotes formation of metastable, nanometer-scale TiO2 brookite rather than the more common anatase and rutile TiO2 polymorphs typically produced through MOF pyrolysis at high temperature. These findings highlight the potential to harness the MOF topology and chemical environment to design and control node distortions and enable access to exotic metastable nanoparticle states.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Iodine Close Packing in Hybrid Halide Bismuth(III) and Antimony(III) Semiconductors: (NH 3 (CH 2 ) 7 NH 3 ) 2 Bi 2 I 10 and (NH 3 (CH 2 ) 7 NH 3 ) 2 Sb 2 I 10
Simple features in complex hybrid inorganic–organic crystalline materials provide opportunities for targeted discovery of materials with desired optoelectronic properties. In this study, we report the structure and optoelectronic properties of isostructural (NH 3 (CH 2 ) 7 NH 3 ) 2 Bi 2 I 10 and (NH 3 (CH 2 ) 7 NH 3 ) 2 Sb 2 I 10 . The crystal structures are characterized by corner-connected metal-iodide octahedral chains that form a cubic close-packed iodine inorganic framework. Variable temperature UV–visible diffuse reflectance spectroscopy reveals stark contrasts in the onset of absorption and color changes between the [MX6]3– based structures, due to differences in the interaction of the (NH 3 (CH 2 ) 7 NH 3 ) 2+ organic ammonium cation and the iodine packing of the inorganic framework. Density functional theory (DFT) calculations reveal flat bands reflective of the pseudo-1D crystal structure. Dark microwave conductivity (DMC) and time-resolved microwave conductivity (TRMC) reveal an excitonic character with long carrier lifetimes, consistent with the electronically confined octahedral chains. Comparison of the structural features with those of other diammonium-containing crystals reveals that diammoniumheptane can substitute into structures, displacing inorganic octahedra while retaining a close-packed anion framework. This provides a means for targeting new hybrid materials in which “vacancy-ordering” provides a crystal chemical approach for targeting desirable optoelectronic properties.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Underlying mechanism of structural transformation between GaSb and GaAs response to intense electronic excitation
Ion irradiation of semiconductors has emerged as a promising approach for fabricating self-organized nanosystems with high atomic precision, despite often being accompanied by undesirable phenomena. Exploring the mechanisms underlying structural transformations is crucial for assessing nanostructure array types under complex irradiation environments. By quantitatively calculating the thermodynamically driven processes and analyzing the impact of intrinsic structural parameters, distinct structural transformations in response to intense electronic excitation are systematically investigated in gallium antimonide (GaSb) and gallium arsenide (GaAs) systems. In high-energy regimes, the nanofibers layer of GaSb exhibits intriguing structural discrepancy, characterized by partial nanofibers with coherent boundaries, interspersed nanopores accompanied by antisite defects and Ga precipitates, distinguishing to a series of discontinuous latent tracks that emerged within cylindrical trajectories in GaAs. Furthermore, significant diffusion behaviors of the nanohillocks are discovered in GaAs, with higher average roughness than GaSb, driven by the gradient stress distribution influenced by the free-surface effects. The deposition energy for melting phase formation, Gibbs free energy, and Ga diffusion coefficients contribute to the distinctive structural features, evidencing relatively stable morphological configurations and higher irradiation resistance in GaAs. Consequently, special optoelectronic properties associated with structural discrepancies facilitate the design and optimization of material functionalities by irradiation technologies.
36 MATERIALS SCIENCE↗
Functional Relevance of CASP16 Nucleic Acid Predictions as Evaluated by Structure Providers
ABSTRACT Accurate biomolecular structure prediction enables the prediction of mutational effects, the speculation of function based on predicted structural homology, the analysis of ligand binding modes, experimental model building, and many other applications. Such algorithms to predict essential functional and structural features remain out of reach for biomolecular complexes containing nucleic acids. Here, we report a quantitative and qualitative evaluation of nucleic acid structures for the CASP16 blind prediction challenge by 12 of the experimental groups who provided nucleic acid targets. Blind predictions accurately model secondary structure and some aspects of tertiary structure, including reasonable global folds for some complex RNAs; however, predictions often lack accuracy in the regions of highest functional importance. All models have inaccuracies in non‐canonical regions where, for example, the nucleic‐acid backbone bends, deviating from an A‐form helix geometry, or a base forms a non‐standard hydrogen bond (not a Watson‐Crick base pair). These bends and non‐canonical interactions are integral to forming functionally important regions such as RNA enzymatic active sites. Additionally, the modeling of conserved and functional interfaces between nucleic acids and ligands, proteins, or other nucleic acids remains poor. For some targets, the experimental structures may not represent the only structure the biomolecular complex occupies in solution or in its functional life cycle, posing a future challenge for the community.
Biochemistry & Molecular Biology↗
Multinuclear Solid-State NMR and NMR Crystallography of Solid Forms of Creatine and Creatinine
Creatine is a performance-enhancing supplement with two widely available commercial solid forms, namely, creatine monohydrate (creatine·H 2 O) and creatine HCl, the latter of which does not have a reported crystal structure. Moreover, commercial formulations of creatine may contain creatinine, an undesired impurity phase resulting from the self-cyclization of creatine during manufacturing. Therefore, reliable methods for characterizing the different solid forms of creatine and detecting the presence of creatinine are essential. Herein, we address these challenges using 13 C, 15 N, and 35 Cl solid-state NMR (SSNMR) spectroscopy to obtain distinct spectral fingerprints for creatine·H 2 O and creatine HCl, along with creatinine and creatinine HCl. The acquisition of these SSNMR spectra offers a robust approach for both the rapid characterization of each solid form and the detection of the impurity phases. Additionally, quadrupolar NMR crystallography-guided crystal structure prediction (QNMRX-CSP) was applied for the de novo crystal structure determination of creatine HCl, which was validated by the subsequently determined single-crystal X-ray diffraction (SCXRD) structure. Finally, to investigate the relationship between NMR parameters and structural features, 13 C and 15 N chemical shifts and 35 Cl electric field gradient (EFG) tensors were computed from geometry-optimized structures of the four solid forms by using dispersion-corrected DFT-D2* methods. Finally, this integrative approach offers a powerful framework for advancing the structural understanding and quality control of creatine-based supplements and next-generation formulations, as well as a wide range of other solid pharmaceuticals and nutraceuticals.
NMR↗
Simultaneous occupancy of Cu C and Cu D in the ammonia monooxygenase active site
Ammonia monooxygenase (AMO), a copper-dependent membrane enzyme, catalyzes the first and rate-limiting step of nitrification: the oxidation of ammonia to hydroxylamine. Despite its central role in the global nitrogen cycle and its biotechnological relevance, structural characterization of AMO has lagged behind that of its homolog, particulate methane monooxygenase (pMMO), due to the slow growth rates of ammonia-oxidizing bacteria and the instability of AMO upon purification. Recent cryoEM studies of Nitrosomonas europaea AMO and Methylococcus capsulatus (Bath) pMMO in native membranes revealed new structural features, including two adjacent copper-binding sites in the transmembrane region, Cu C and Cu D , believed to constitute the active site. Although multiple structures were determined under various conditions, simultaneous occupancy of Cu C and Cu D was never observed, leaving their potential functional interplay unresolved. Here we report the 2.6 Å resolution cryoEM structure of AMO from Nitrosospira briensis C-128 in isolated native membranes. This structure reveals the first instance of simultaneous copper occupancy of the Cu C and Cu D sites, along with occupancy of the periplasmic Cu B site. Electron paramagnetic resonance (EPR) spectroscopic data indicate that the Cu B site is primarily occupied by Cu(II), while Cu C and Cu D are primarily occupied by diamagnetic ions, presumably Cu(I). Notably, a lipid molecule is bound between the Cu C and Cu D sites, separating them by ∼8.0 Å. The results underscore the importance of studying these enzymes in their native environments across species to resolve conserved and divergent molecular features.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Advancing Mn-Based Li-Ion Battery Cathodes via a Partially Cation-Disordered Zigzag-Type Li–Nb–Mn–O Framework
Mn-based Li-ion battery cathodes encompass a great variety of materials structures. Decades of research effort have proven that developing a Mn-based structure featuring a high redox activity, stable cycling, and cost-effectiveness is a longstanding challenge. Motivated by such a need and inspired by the structural diversity of Mn-based cathodes, we develop a partially cation-disordered lithium niobium manganese oxide with a zigzag structure, filling the knowledge gap between zigzag-ordered and fully disordered Li-Mn-based oxides. Electrochemically, the partially disordered cathode greatly unlocks the redox activity of the zigzag lattice and maintains the cycling stability. Mechanism-wise, the partial disordering suppresses the disproportionation reaction of Mn(III) and facilitates a disordered λ-MnO 2 -tetragonal cation-disordered rock salt structural transformation. Furthermore, the work suggests the substantial opportunity of using partial disordering as the key strategy to revive locked-up redox activities and realize new energy storage mechanisms, for the pursuit of high-performance cost-effective battery materials.
25 ENERGY STORAGE↗
Structure–performance relationships of lithium-ion battery cathodes revealed by contrast-variation small-angle neutron scattering
Lithium-ion battery cathodes are porous composites of active material, conductive carbon, and polymer binder. Controlling the cathode microstructure is key to achieving high energy density and cycling stability. Current characterization techniques lack the nanoscale resolution over representative volumes necessary to relate cathode microstructure to cycling performance. To address this challenge, we utilize contrast-variation small-angle neutron scattering to quantify the chemical and structural features of cathodes wet by dimethyl carbonate, representing a relevant solvent environment. Using neutron scattering measurements, we identify an expansion in carbon and polymer structures that arises after calendering and wetting with solvent. Further, we deconvolute the carbon and binder phases to obtain the solvent-accessible carbon black surface area, which we correlate to diminished capacity retention driven by electrolyte decomposition on exposed carbon. This technique provides nanoscale insight into composite cathode microstructures and resulting cycling performance, promising future applications to a broad range of porous materials that exist in energy storage systems.
25 ENERGY STORAGE↗
PNNL-Predictive-Phenomics/ProCaliper
ProCaliper is a Python library that curates, organizes, and computes protein structure features in a way that easily interfaces with user-provided experimental data. It extracts or computes protein binding site, active site, charge, pLDDT (order/disorder), acid dissociation, protonation, solvent accessible surface area, disulfide bond distance, and protein secondary structure data using precomputed protein structures and publicly available databases. It provides a unified API for integrating additional residue-level data and for visualizing residue features in 3D.
Rozum, Jordan [Pacific Northwest National Lab]↗
Active Learning Surrogates for Integrating Electron Microscopy and Computational Insights from Simulations in Autonomous Experiments
Artificial Intelligence (AI) combined with simulations and experiments has great potential to accelerate scientific discovery across technology and pharmaceuticals. However, the gap between simulations and experiments is challenging due to disparities in time and scale, making it difficult to estimate properties like energy and electronic states from experiments, and to provide feedback based on theoretical insights.Our research addresses the challenge by developing unique deep kernel based surrogate models that learns from microscopic images, mapping structural features to energy differences from defect formation. We start with full-training using simulated images to determine optimal settings, establishing a baseline for active learning. Using these settings from the baseline, active learning is trained, and predicts structures along simulation trajectories based on uncertainty and energetic stability, thus reducing data requirements, simulation time and computational costs. The results demonstrate that the model achieves a low average error margin of approximately 0.03 meV, indicating good performance. To enhance feature extraction and reconstruction capabilities, we developed an autoencoder-decoder as additional surrogate to create latent space to capture essential features, enabling precise comparisons between simulations and experiments. The results from this model achieved a reconstruction loss of around 0.2 and accurately reconstructed molecular structures.Overall, this work advances the steering of experiments through computational simulations by employing a surrogate models that actively predicts the trajectories of structural evolution, achieving time-to-solution comparable to experimental measurements.
Saranathan, Gayathri [Hewlett-Packard]↗
d -Electron Heavy Fermion Behavior in a Near-Room-Temperature Polar Metallic Ferrimagnet: A Case of Mn 5 SiC
Polycrystalline Mn 5 SiC was synthesized by using a high-temperature solid-state method. Mn 5 SiC adopts a polar space group (Cmc2 1 ) with six crystallographic Mn sites confirmed by X-ray and neutron diffraction, transmission electron microscopy, and second harmonic generation experiments. The complex crystal structure features edge-sharing trigonal prisms and icosahedra, as well as face/edge-sharing pentagonal prisms. Magnetic measurements indicate ferrimagnetic ordering with a transition temperature of 284 K. The ferrimagnetic structure (magnetic space group Cm’c’2 1 ) was further identified by powder neutron diffraction, where collinear Mn spins align along the crystallographic c-axis. The refined magnetic moment for each crystallographic Mn site at 4 K is 1.8(2), −2.42(9), −1.72(8), 0.51(6), 0.50(4), and 1.7(2) μ B . Density functional theory calculations confirm both the metallic behavior and the ferrimagnetic structure observed experimentally and further provide insight into the observed Mn moment dependence across crystallographic sites. The resistivity and specific heat measurements and density functional theory calculations reveal a substantially large Kadowaki–Woods ratio of 5 × 10 –5 μΩ·cm/(mJ/mol) 2 and a many-body renormalization factor of 5.5, indicating the unusual heavy Fermion behavior in such an itinerant magnetic metal.
Messegee, Zachary T. [George Mason University, Fai↗
MultiTaskDeltaNet: change detection-based image segmentation for operando ETEM with application to carbon gasification kinetics
Transforming in situ transmission electron microscopy (TEM) imaging into a tool for spatially-resolved operando characterization of solid-state reactions requires automated, high-precision semantic segmentation of dynamically evolving features. However, traditional deep learning methods for semantic segmentation often face limitations due to the scarcity of labeled data, visually ambiguous features of interest, and scenarios involving small objects. To tackle these challenges, we introduce MultiTaskDeltaNet (MTDN), a novel deep learning architecture that creatively reconceptualizes the segmentation task as a change detection problem. By implementing a unique Siamese network with a U-Net backbone and using paired images to capture feature changes, MTDN effectively leverages minimal data to produce high-quality segmentations. Furthermore, MTDN utilizes a multi-task learning strategy to exploit correlations between physical features of interest. In an evaluation using data from in situ environmental TEM (ETEM) videos of filamentous carbon gasification, MTDN demonstrated a significant advantage over conventional segmentation models, particularly in accurately delineating fine structural features. Notably, MTDN achieved a 10.22% performance improvement over conventional segmentation models in predicting small and visually ambiguous physical features. This work bridges key gaps between deep learning and practical TEM image analysis, advancing automated characterization of nanomaterials in complex experimental settings.
08 HYDROGEN↗