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Using a large-scale, experimentally captured 3D microstructure data set, we implement the generative adversarial network (GAN) framework to learn and generate 3D microstructures of solid oxide fuel cell electrodes. The generated microstructures are visually, statistically, and topologically realistic, with distributions of microstructural parameters, including volume fraction, particle size, surface area, tortuosity, and triple-phase boundary density, being highly similar to those of the original microstructure. These results are compared and contrasted with those from an established, grain-based generation algorithm (DREAM.3D). Importantly, simulations of electrochemical performance, using a locally resolved finite element model, demonstrate that the GAN-generated microstructures closely match the performance distribution of the original, while DREAM.3D leads to significant differences. The ability of the generative machine learning model to recreate microstructures with high fidelity suggests that the essence of complex microstructures may be captured and represented in a compact and manipulatable form.

Quantifying the level of atomic disorder within materials is critical to understanding how evolving local structural environments dictate performance and durability. Here, we leverage graph neural networks to define a physically interpretable metric for local disorder, called SODAS. This metric encodes the diversity of the local atomic configurations as a continuous spectrum between the solid and liquid phases, quantified against a distribution of thermal perturbations. We apply this methodology to four prototypical examples with varying levels of disorder: (1) grain boundaries, (2) solid-liquid interfaces, (3) polycrystalline microstructures, and (4) tensile failure/fracture. We also compare SODAS to several commonly used methods. Using elemental aluminum as a case study, we show how our paradigm can track the spatio-temporal evolution of interfaces, incorporating a mathematically defined description of the spatial boundary between order and disorder. We further show how to extract physics-preserved gradients from our continuous disorder fields, which may be used to understand and predict materials performance and failure. Overall, our framework provides a simple and generalizable pathway to quantify the relationship between complex local atomic structure and coarse-grained materials phenomena. Level of atomic disorder in materials is critical to understanding the effect of local structure on materials properties. Here the authors present a workflow combining structure-aware graph neural networks and physics-inspired order parameter to characterize structural disorder on a per atom basis.

Gastric motility is coordinated by bioelectrical slow-wave activity, and abnormal electrical dysrhythmias have been associated with nausea and vomiting. Studies have often been conducted under general anaesthesia, while the impact of general anaesthesia on slow-wave activity has not been studied. Clinical studies have shown that propofol anaesthesia reduces postoperative nausea and vomiting (PONV) compared with isoflurane, while the underlying mechanisms remain unclear. In this study, we investigated the effects of two anaesthetic drugs, intravenous (IV) propofol and volatile isoflurane, on slow-wave activity. In vivo experiments were performed in female weaner pigs ( n  = 24). Zolazepam and tiletamine were used to induce general anaesthesia, which was maintained using either IV propofol ( n  = 12) or isoflurane ( n  = 12). High-resolution electrical mapping of slow-wave activity was performed. Slow-wave dysrhythmias occurred less often in the propofol group, both in the duration of the recorded period that was dysrhythmic (propofol 14 ± 26%, isoflurane 43 ± 39%, P  = 0.043 (Mann–Whitney U test)), and in a case-by-case basis (propofol 3/12, isoflurane 8/12, P  = 0.015 (Chi-squared test)). Slow-wave amplitude was similar, while velocity and frequency were higher in the propofol group than the isoflurane group ( P  < 0.001 (Student’s t- test)). This study presents a potential physiological biomarker linked to recent observations of reduced PONV with IV propofol. The results suggest that propofol is a more suitable anaesthetic for studying slow-wave patterns in vivo.

Introduction While most gastric bypass patients recover well, some experience long-term complications, including nausea, abdominal pain, food intolerance, and dumping. This study aimed to evaluate symptoms and quality of life (QoL) in association with the residual activity of the remnant stomach. Methods Patients undergoing gastric bypass and conversion-to-bypass were recruited. The Gastric Alimetry® System (Auckland, NZ) was employed, comprising a high-resolution electrode array, wearable reader, and validated symptom logging app. The protocol comprised 30-min fasting baseline, a 218-kCal meal stimulus, and 4-h of post-prandial recordings. Symptoms and QoL were evaluated using validated questionnaires. Remnant gastric electrophysiology evaluation included frequency, BMI-adjusted amplitude, and Gastric Alimetry Rhythm Index (GA-RI, reflecting pacemaker stability), with comparison to validated reference intervals and matched controls. Results Thirty-eight participants were recruited with mean time from bypass 46.8 ± 28.6 months. One-third of patients showed moderate to severe post-prandial symptoms, with patients’ median PAGI-SYM 28 ± 19 vs controls 9 ± 17 ( p  < 0.01); PAGI-QOL 37 ± 31 vs 135 ± 22 ( p  < 0.0001). Remnant gastric function was markedly degraded shown by undetectable frequencies in 84% (vs 0% in controls) and low GA-RI (0.18 ± 0.08 vs 0.51 ± 0.22 in controls; p  < 0.0001; reference range > 0.25). Impaired GA-RI and amplitude were correlated with worse PAGI-SYM and PAGI-QOL scores. Conclusion One-third of post-bypass patients suffered significant upper GI symptoms with reduced QoL. The bypassed remnant stomach shows highly deranged electrophysiology in-situ, reflecting disuse degeneration. These derangements correlated with QoL; however, causality is not implied by the present study.

Loss of interstitial cells of Cajal (ICC) has been associated with gastric dysfunction and is also observed during normal aging at ~13% reduction per decade. The impact of ICC loss on gastric slow wave conduction velocity is currently undefined. This study correlated human gastric slow wave velocity with ICC loss and aging. High‐resolution gastric slow wave mapping data were screened from a database of 42 patients with severe gastric dysfunction (n = 20) and controls (n = 22). Correlations were performed between corpus slow wave conduction parameters (frequency, velocity, and amplitude) and corpus ICC counts in patients, and with age in controls. Physiological parameters were further integrated into computational models of gastric mixing. Patients: ICC count demonstrated a negative correlation with slow wave velocity in the corpus (i.e., higher velocities with reduced ICC; r2 = .55; p = .03). ICC count did not correlate with extracellular slow wave amplitude (p = .12) or frequency (p = .84). Aging: Age was positively correlated with slow wave velocity in the corpus (range: 25–74 years; r2 = .32; p = .02). Age did not correlate with extracellular slow wave amplitude (p = .40) or frequency (p = .34). Computational simulations demonstrated that the gastric emptying rate would increase at higher slow wave velocities. ICC loss and aging are associated with a higher slow wave velocity. The reason for these relationships is unexplained and merit further investigation. Increased slow wave velocity may modulate gastric emptying higher, although in gastroparesis other pathological factors must dominate to prevent emptying. Loss of interstitial cells of Cajal and aging is associated with a higher gastric slow wave velocity. This study used human data for correlation and computational models to determine the effects on gastric emptying and mixing. The reason for these relationships is unexplained and merit further investigation.

Although multiple oxide-based solid electrolyte materials with intrinsically high ionic conductivities have emerged, practical processing and synthesis routes introduce grain boundaries and other interfaces that can perturb primary conduction channels. To directly probe these effects, we demonstrate an efficient and general mesoscopic computational method capable of predicting effective ionic conductivity through a complex polycrystalline oxide-based solid electrolyte microstructure without relying on simplified equivalent circuit description. We parameterize the framework for Li7-xLa3Zr2O12 (LLZO) garnet solid electrolyte by combining synthetic microstructures from phase-field simulations with diffusivities from molecular dynamics simulations of ordered and disordered systems. Systematically designed simulations reveal an interdependence between atomistic and mesoscopic microstructural impacts on the effective ionic conductivity of polycrystalline LLZO, quantified by newly defined metrics that characterize the complex ionic transport mechanism. Our results provide fundamental understanding of the physical origins of the reported variability in ionic conductivities based on an extensive analysis of literature data, while simultaneously outlining practical design guidance for achieving desired ionic transport properties based on conditions for which sensitivity to microstructural features is highest. Additional implications of our results are discussed, including a possible connection between ion conduction behavior and dendrite formation.

We propose an effective method for removing thermal vibrations that complicate the task of analyzing complex dynamics in atomistic simulation of condensed matter. Our method iteratively subtracts thermal noises or perturbations in atomic positions using a denoising score function trained on synthetically noised but otherwise perfect crystal lattices. The resulting denoised structures clearly reveal underlying crystal order while retaining disorder associated with crystal defects. Purely geometric, agnostic to interatomic potentials, and trained without inputs from explicit simulations, our denoiser can be applied to simulation data generated from vastly different interatomic interactions. The denoiser is shown to improve existing classification methods, such as common neighbor analysis and polyhedral template matching, reaching perfect classification accuracy on a recent benchmark dataset of thermally perturbed structures up to the melting point. Demonstrated here in a wide variety of atomistic simulation contexts, the denoiser is general, robust, and readily extendable to delineate order from disorder in structurally and chemically complex materials.

Spectroscopy techniques such as x-ray absorption near edge structure (XANES) provide valuable insights into the atomic structures of materials, yet the inverse prediction of precise structures from spectroscopic data remains a formidable challenge. In this study, we introduce a framework that combines generative artificial intelligence models with XANES spectroscopy to predict three-dimensional atomic structures of disordered systems, using amorphous carbon ( a -C) as a model system. In this work, we introduce a new framework based on the diffusion model, a recent generative machine learning method, to predict 3D structures of disordered materials from a target property. For demonstration, we apply the model to identify the atomic structures of a -C as a representative material system from the target XANES spectra. We show that conditional generation guided by XANES spectra reproduces key features of the target structures. Furthermore, we show that our model can steer the generative process to tailor atomic arrangements for a specific XANES spectrum. Finally, our generative model exhibits a remarkable scale-agnostic property, thereby enabling generation of realistic, large-scale structures through learning from a small-scale dataset (i.e. with small unit cells). Our work represents a significant stride in bridging the gap between materials characterization and atomic structure determination; in addition, it can be leveraged for materials discovery in exploring various material properties as targeted.

Graph neural networks are attractive for learning properties of atomic structures thanks to their intuitive graph encoding of atoms and bonds. However, conventional encoding does not include angular information, which is critical for describing atomic arrangements in disordered systems. In this work, we extend the recently proposed ALIGNN (Atomistic Line Graph Neural Network) encoding, which incorporates bond angles, to also include dihedral angles (ALIGNN-d). This simple extension leads to a memory-efficient graph representation that captures the complete geometry of atomic structures. ALIGNN-d is applied to predict the infrared optical response of dynamically disordered Cu(II) aqua complexes, leveraging the intrinsic interpretability to elucidate the relative contributions of individual structural components. Bond and dihedral angles are found to be critical contributors to the fine structure of the absorption response, with distortions that represent transitions between more common geometries exhibiting the strongest absorption intensity. Future directions for further development of ALIGNN-d are discussed.