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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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We developed models to predict the photoactivity of organic molecules for photocatalytic hydrogen evolution by integrating experiment, computation, and machine learning. This marks a step toward the data-driven discovery of molecular photocatalysts.
Imaging, scattering, and spectroscopy are fundamental in understanding and discovering new functional materials. Contemporary innovations in automation and experimental techniques have led to these measurements being performed much faster and with higher resolution, thus producing vast amounts of data for analysis. These innovations are particularly pronounced at user facilities and synchrotron light sources. Machine learning (ML) methods are regularly developed to process and interpret large datasets in real-time with measurements. However, there remain conceptual barriers to entry for the facility general user community, whom often lack expertise in ML, and technical barriers for deploying ML models. Herein, we demonstrate a variety of archetypal ML models for on-the-fly analysis at multiple beamlines at the National Synchrotron Light Source II (NSLS-II). We describe these examples instructively, with a focus on integrating the models into existing experimental workflows, such that the reader can easily include their own ML techniques into experiments at NSLS-II or facilities with a common infrastructure. The framework presented here shows how with little effort, diverse ML models operate in conjunction with feedback loops via integration into the existing Bluesky Suite for experimental orchestration and data management.
The study presents a scalable and sustainable extraction method for rare earth, featuring a green, scalable synthesis of diglycolamides that reduces greenhouse gas emissions by 67% and a liquid-liquid extraction workflow.
PSI4 is a free and open-source ab initio electronic structure program providing implementations of Hartree–Fock, density functional theory, many-body perturbation theory, configuration interaction, density cumulant theory, symmetry-adapted perturbation theory, and coupled-cluster theory. Most of the methods are quite efficient, thanks to density fitting and multi-core parallelism. The program is a hybrid of C++ and Python, and calculations may be run with very simple text files or using the Python API, facilitating post-processing and complex workflows; method developers also have access to most of PSI4 's core functionalities via Python. Job specification may be passed using The Molecular Sciences Software Institute (MolSSI) QCSCHEMA data format, facilitating interoperability. We announce a rewrite of our top-level computation driver, and concomitant adoption of the MolSSI QCARCHIVE INFRASTRUCTURE project, makes the latest version of PSI4 well suited to distributed computation of large numbers of independent tasks. The project has fostered the development of independent software components that may be reused in other quantum chemistry programs.
Non-negative matrix factorization (NMF) is an appealing class of methods for performing unsupervised learning on streaming spectral data, particularly in time-sensitive applications such as in situ characterization of materials. These methods seek to decompose a dataset into a small number of components and weights that can compactly represent the underlying signal while effectively reconstructing the observations with minimal error. However, canonical NMF methods have no underlying requirement that the reconstruction uses components or weights that are representative of the true physical processes. In this work, we demonstrate how constraining a subset of the NMF weights or components as rigid priors, provided as known or assumed values, can provide significant improvement in revealing true underlying phenomena. We present a PyTorch-based method for efficiently applying constrained NMF and demonstrate its application to several synthetic examples. Our implementation allows an expert researcher-in-the-loop to provide and dynamically adjust the constraints during a live experiment involving streaming spectral data. Such interactive priors allow researchers to specify known or identified independent components, as well as functional expectations about the mixing or transitions between the components. We further demonstrate the application of this method to measured synchrotron x-ray total scattering data from in situ beamline experiments. In such a context, constrained NMF can result in a more interpretive and scientifically relevant decomposition than canonical NMF or other decomposition techniques. As a result, the details of the method are provided, along with general guidance for employing constrained NMF in the extraction of critical information and insights during time-sensitive experimental applications.
Monolayer films have shown promise as a lubricating layer to reduce friction and wear of mechanical devices with separations on the nanoscale. These films have a vast design space with many tunable properties that can affect their tribological effectiveness. For example, terminal group chemistry, film composition, and backbone chemistry can all lead to films with significantly different tribological properties. This design space, however, is very difficult to explore without a combinatorial approach and an automatable, reproducible, and extensible workflow to screen for promising candidate films. Here, using the Molecular Simulation Design Framework (MoSDeF), a combinatorial screening study was performed to explore 9747 unique monolayer films (116 964 total simulations) and a machine learning (ML) model using a random forest regressor, an ensemble learning technique, to explore the role of terminal group chemistry and its effect on tribological effectiveness. The most promising films were found to contain small terminal groups such as cyano and ethylene. The ML model was subsequently applied to screen terminal group candidates identified from the ChEMBL small molecule library. Approximately 193 131 unique film candidates were screened with approximately a five order of magnitude speed-up in analysis compared to simulation alone. The ML model was thus able to be used as a predictive tool to greatly speed up the initial screening of promising candidate films for future simulation studies, suggesting that computational screening in combination with ML can greatly increase the throughput in combinatorial approaches to generate in silico data and then train ML models in a controlled, self-consistent fashion.
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Significance We report a methodology for the pooled construction of mutants bearing precise genomic sequence variations and multiplex phenotypic characterization of these mutants using next-generation sequencing (NGS). Unlike existing techniques depending on CRISPR-Cas–directed genomic breaks for genome editing, this strategy instead uses single-stranded DNA produced by a retron element for recombineering. This enables libraries of millions of elements to be constructed and offers relaxed design constraints which permit natural DNA or random variation to be used as inputs.
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Demands on Field-Programmable Gate Array (FPGA) data transport have been increasing over the years as frame sizes and refresh rates increase. As the bandwidths requirements increase the ability to implement data transport protocol layers using "soft" programmable logic becomes harder and start to require harden IP blocks implementation. To reduce the number of physical links and interconnects, it is common for data acquisition systems to require interleaving of streams on the same link (e.g. streaming data and streaming register access). Further, this paper presents a way to leverage existing FPGA harden IP blocks to achieve a robust, low latency 100 Gb/s point-to-point link with minimal programmable logic overhead geared towards the needs of data acquisition systems with interleaved streaming requirements.
Abstract Neurospora crassa propagates through dissemination of conidia, which develop through specialized structures called conidiophores. Recent work has identified striking variation in conidiophore morphology, using a wild population collection from Louisiana, United States of America to classify 3 distinct phenotypes: Wild-Type, Wrap, and Bulky. Little is known about the impact of these phenotypes on sporulation or germination later in the N. crassa life cycle, or about the genetic variation that underlies them. In this study, we show that conidiophore morphology likely affects colonization capacity of wild N. crassa isolates through both sporulation distance and germination on different carbon sources. We generated and crossed homokaryotic strains belonging to each phenotypic group to more robustly fit a model for and estimate heritability of the complex trait, conidiophore architecture. Our fitted model suggests at least 3 genes and 2 epistatic interactions contribute to conidiophore phenotype, which has an estimated heritability of 0.47. To uncover genes contributing to these phenotypes, we performed RNA-sequencing on mycelia and conidiophores of strains representing each of the 3 phenotypes. Our results show that the Bulky strain had a distinct transcriptional profile from that of Wild-Type and Wrap, exhibiting differential expression patterns in clock-controlled genes (ccgs), the conidiation-specific gene con-6, and genes implicated in metabolism and communication. Combined, these results present novel ecological impacts of and differential gene expression underlying natural conidiophore morphological variation, a complex trait that has not yet been thoroughly explored.
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Abstract The rapid design and assembly of synthetic DNA constructs have become a crucial component of biological engineering projects via iterative design–build–test–learn cycles. In this perspective, we provide an overview of the workflows used to generate the thousands of constructs and libraries produced each year at the U.S. Department of Energy Joint Genome Institute. Particular attention is paid to describing pipelines, tools used, types of scientific projects enabled by the platform and challenges faced in further scaling output.
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The performance and integrity of photovoltaic (PV) modules and materials may be assessed through optical characterizations. We have developed a custom optical mapping instrument (OMI) to improve measurement throughput time, facilitate automated measurements, allow for localized specimen examination, obtain spatial information relative to commercial benchtop spectrophotometer instruments, and provide automated data analysis. Both the key components and the methods used to improve the measurements are described. The measurement precision for the OMI is benchmarked for hemispherical transmittance relative to a previous round-robin study for commercial spectrophotometers. In addition to the unique instrument developed in this study, new insights have emerged from the examination of specimens from previous experiments, including: the depletion of the UV absorber is revealed for specimens beyond a certain size after artificial ultraviolet (UV) weathering; an optical degradation by internal haze formation is confirmed following Damp Heat (85 °C/85% relative humidity) testing; the better uniformity of the wear pattern for falling sand abrasion is used to select the DIN 52348 fixture over the ASTM D968 fixture; and the greater local (in the order of millimeters) heterogeneity of surface contamination in the soiling-prone location of Dubai relative to Kuwait. The results of the UV study have already been used to improve the standardized weathering of encapsulants. The example for Damp Heat testing is given to increase awareness of a degradation believed to be ubiquitously occurring in safety and design-type qualification testing of PV modules.
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