Machine learning for classifying and interpreting coherent X-ray speckle patterns

Speckle patterns produced by coherent X-ray have a close relationship with the internal structure of materials but quantitative inversion of the relationship to determine structure from speckle patterns is challenging. Here, we investigate the link between coherent X-ray speckle patterns and sample structures using a model 2D disk system and explore the ability of machine learning to learn aspects of the relationship. Specifically, we train a deep neural network to classify the coherent X-ray speckle patterns according to the disk number density in the corresponding structure. It is demonstrated that the classification system is accurate for both non-disperse and disperse size distributions

Evolutionary optimization of a charge transfer ionic potential model for Ta/Ta-oxide hetero-interfaces

Tantalum, tantalum oxide and their hetero-interfaces are of tremendous technological interest in several applications spanning electronics, thermal management, catalysis and biochemistry. For example, local oxygen stoichiometry variation in TaOx memristors comprising of metallic (Ta) and insulating oxide (Ta2O5) have been shown to result in fast switching on the sub-nanosecond timescale over a billion cycles, relevant to neuromorphic computation. Despite its broad importance, an atomistic scale understanding of oxygen stoichiometry variation across Ta/TaOx hetero-interfaces, such as during early stages of oxidation and oxide growth, is not well understood. This is mainly due to the lack of a variable charge interatomic potential model for tantalum oxides that can accurately describe the ionic interactions in the metallic (Ta) and oxide (TaOx) environment as well as at their interfaces. To address this challenge, we introduce a charge transfer ionic potential (CTIP) model for Ta/Ta-oxide system by training against lattice parameters, cohesive energies, equations of state, and elastic properties of various experimentally observed Ta2O5 polymorphs. The best set of CTIP parameters are determined by employing a single-objective global optimization scheme driven by genetic algorithms followed by local Simplex optimization. Our newly developed CTIP potential accurately predicts structure, thermodynamics, energetic ordering of polymorphs, as well as elastic and surface properties of both Ta and Ta2O5, in excellent agreement with DFT calculations and experiments. We employ our newly parameterized CTIP potential to investigate the early stages of oxidation of Ta at different temperatures and atomic/molecular nature of the oxidizing species

Machine Learning on Neutron and X-Ray Scattering

Neutron and X-ray scattering represent two state-of-the-art materials characterization techniques that measure materials' structural and dynamical properties with high precision. These techniques play critical roles in understanding a wide variety of materials systems, from catalysis to polymers, nanomaterials to macromolecules, and energy materials to quantum materials. In recent years, neutron and X-ray scattering have received a significant boost due to the development and increased application of machine learning to materials problems. This article reviews the recent progress in applying machine learning techniques to augment various neutron and X-ray scattering techniques. We highlight the integration of machine learning methods into the typical workflow of scattering experiments. We focus on scattering problems that faced challenge with traditional methods but addressable using machine learning, such as leveraging the knowledge of simple materials to model more complicated systems, learning with limited data or incomplete labels, identifying meaningful spectra and materials' representations for learning tasks, mitigating spectral noise, and many others. We present an outlook on a few emerging roles machine learning may play in broad types of scattering and spectroscopic problems in the foreseeable future.Comment: 56 pages, 12 figures. Feedback most welcom

Fusion-dependent formation of lipid nanoparticles containing macromolecular payloads

The success of Onpattro™ (patisiran) clearly demonstrates the utility of lipid nanoparticle (LNP) systems for enabling gene therapies. These systems are composed of ionizable cationic lipids, phospholipid, cholesterol, and polyethylene glycol (PEG)-lipids, and are produced through rapid-mixing of an ethanolic-lipid solution with an acidic aqueous solution followed by dialysis into neutralizing buffer. A detailed understanding of the mechanism of LNP formation is crucial to improving LNP design. Here we use cryogenic transmission electron microscopy and fluorescence techniques to further demonstrate that LNP are formed through the fusion of precursor, pH-sensitive liposomes into large electron-dense core structures as the pH is neutralized. Next, we show that the fusion process is limited by the accumulation of PEG-lipid on the emerging particle. Finally, we show that the fusion-dependent mechanism of formation also applies to LNP containing macromolecular payloads including mRNA, DNA vectors, and gold nanoparticles

Hematopoietic Cell Transplantation in Patients With Primary Immune Regulatory Disorders (PIRD): A Primary Immune Deficiency Treatment Consortium (PIDTC) Survey.

Primary Immune Regulatory Disorders (PIRD) are an expanding group of diseases caused by gene defects in several different immune pathways, such as regulatory T cell function. Patients with PIRD develop clinical manifestations associated with diminished and exaggerated immune responses. Management of these patients is complicated; oftentimes immunosuppressive therapies are insufficient, and patients may require hematopoietic cell transplant (HCT) for treatment. Analysis of HCT data in PIRD patients have previously focused on a single gene defect. This study surveyed transplanted patients with a phenotypic clinical picture consistent with PIRD treated in 33 Primary Immune Deficiency Treatment Consortium centers and European centers. Our data showed that PIRD patients often had immunodeficient and autoimmune features affecting multiple organ systems. Transplantation resulted in resolution of disease manifestations in more than half of the patients with an overall 5-years survival of 67%. This study, the first to encompass disorders across the PIRD spectrum, highlights the need for further research in PIRD management

Comparing optimization strategies for force field parameterization

Classical molecular dynamics (MD) simulations enable modeling of materials and examination of microscopic details that are not accessible experimentally. The predictive capability of MD relies on the force field (FF) used to describe interatomic interactions. FF parameters are typically determined to reproduce selected material properties computed from density functional theory (DFT) and/or measured experimentally. A common practice in parameterizing FFs is to use least-squares local minimization algorithms. Genetic algorithms (GAs) have also been demonstrated as a viable global optimization approach, even for complex FFs. However, an understanding of the relative effectiveness and efficiency of different optimization techniques for the determination of FF parameters is still lacking. In this work, we evaluate various FF parameter optimization schemes, using as example a training data set calculated from DFT for different polymorphs of Ir$O_2$. The Morse functional form is chosen for the pairwise interactions and the optimization of the parameters against the training data is carried out using (1) multi-start local optimization algorithms: Simplex, Levenberg-Marquardt, and POUNDERS, (2) single-objective GA, and (3) multi-objective GA. Using random search as a baseline, we compare the algorithms in terms of reaching the lowest error, and number of function evaluations. We also compare the effectiveness of different approaches for FF parameterization using a test data set with known ground truth (i.e generated from a specific Morse FF). We find that the performance of optimization approaches differs when using the Test data vs. the DFT data. Overall, this study provides insight for selecting a suitable optimization method for FF parameterization, which in turn can enable more accurate prediction of material properties and chemical phenomena

In vivo proton magnetic resonance spectroscopy reveals region specific metabolic responses to SIV infection in the macaque brain

<p>Abstract</p> <p>Background</p> <p><it>In vivo </it>proton magnetic resonance spectroscopy (¹H-MRS) studies of HIV-infected humans have demonstrated significant metabolic abnormalities that vary by brain region, but the causes are poorly understood. Metabolic changes in the frontal cortex, basal ganglia and white matter in 18 SIV-infected macaques were investigated using MRS during the first month of infection.</p> <p>Results</p> <p>Changes in the N-acetylaspartate (NAA), choline (Cho), <it>myo</it>-inositol (MI), creatine (Cr) and glutamine/glutamate (Glx) resonances were quantified both in absolute terms and relative to the creatine resonance. Most abnormalities were observed at the time of peak viremia, 2 weeks post infection (wpi). At that time point, significant decreases in NAA and NAA/Cr, reflecting neuronal injury, were observed only in the frontal cortex. Cr was significantly elevated only in the white matter. Changes in Cho and Cho/Cr were similar across the brain regions, increasing at 2 wpi, and falling below baseline levels at 4 wpi. MI and MI/Cr levels were increased across all brain regions.</p> <p>Conclusion</p> <p>These data best support the hypothesis that different brain regions have variable intrinsic vulnerabilities to neuronal injury caused by the AIDS virus.</p