Packing motifspatterns in how molecules orient relative to one another in a crystal structureare an important concept in many subdisciplines of materials science because of correlations observed ...between specific packing motifs and properties of interest. That said, packing motif data sets have remained small and noisy due to intensive manual labeling processes and insufficient labeling schemes. The most prominent labeling algorithms calculate relative interplanar angles of nearest neighbor molecules to determine the packing motif of a molecular crystal, but this simple approach can fail when neighbors are naively sampled isotropically around the crystal structure. To remedy this issue, we propose an optimization algorithm, which rotates the molecular crystal structure to find representative molecules that inform the packing motif. We package this algorithm into an automated frameworkAutopackwhich both optimally rotates the crystal structure and labels the packing motif based on the appropriate neighboring molecules. In this work, we detail the Autopack framework and its performance, which shows improvements compared to previous state-of-the-art labeling methods, providing the first quantitative point of comparison for packing motif labeling algorithms. Furthermore, using Autopack (available at https://ipo.llnl.gov/technologies/software/autopack), we perform the first large-scale study of potential relationships between chemicals’ compositions and packing motifs, which shows that these relationships are more complex than previously hypothesized from studies that used only tens of polycyclic aromatic hydrocarbon molecules. Autopack’s capabilities help pose next steps for crystal engineering research focusing not only on a molecule’s adoption of a specific packing motif but also on new structure–property relationships.
Herein we report the fabrication of ultralight gold aerogel monoliths with tunable densities and pore structures. Gold nanowires are prepared at the gram scale by substrate-assisted growth with ...uniform size, ultrathin diameters, high purity, and a high aspect ratio. Freeze-casting of suspensions of these nanowires produces free-standing, monolithic aerogels with tunable densities from 6 to 23 mg/cm3, which to the best of our knowledge represents the lowest density monolithic gold material. We also demonstrate that the pore geometries created during freeze-casting can be systematically tuned across multiple length scales by the selection of different solvents and excipients in the feedstock suspension. The mechanical behavior of porous materials depends on relative density and pore architectures.
We explore the application of computer vision and machine learning (ML) techniques to predict material properties (e.g., compressive strength) based on SEM images. We show that it's possible to train ...ML models to predict materials performance based on SEM images alone, demonstrating this capability on the real-world problem of predicting uniaxially compressed peak stress of consolidated molecular solids samples. Our image-based ML approach reduces mean absolute percentage error (MAPE) by an average of 24% over baselines representative of the current state-of-the-practice (i.e., domain-expert's analysis and correlation). We compared two complementary approaches to this problem: (1) a traditional ML approach, random forest (RF), using state-of-the-art computer vision features and (2) an end-to-end deep learning (DL) approach, where features are learned automatically from raw images. We demonstrate the complementarity of these approaches, showing that RF performs best in the “small data” regime in which many real-world scientific applications reside (up to 24% lower RMSE than DL), whereas DL outpaces RF in the “big data” regime, where abundant training samples are available (up to 24% lower RMSE than RF). Finally, we demonstrate that models trained using machine learning techniques are capable of discovering and utilizing informative crystal attributes previously underutilized by domain experts.
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•Mechanical performance of uniaxially compressed solids can be predicted using machine learning on SEM image data.•Computer vision is an effective approach to extract materials attributes for correlating to their performance•Traditional computer vision and machine learning methods are compared with end-to-end deep learning methods. Deep Learning is the more powerful method, provided you have sufficient amount of data•Random forest model performs best in the “small data” regime, whereas deep learning outpaces random forest in the “big data” regime.•In the case of TATB, fine crystal attributes including pores and defects in few micron ranges are strong indicators of material strength
Recent development in proton-exchange membrane fuel cell technology has stimulated research in fuel processing for hydrogen production. Hydrogen can be produced from four different types of methanol ...reforming processes, namely methanol decomposition, partial oxidation of methanol, steam reforming of methanol and oxidative steam reforming of methanol. This review paper discusses commonly used Cu-based catalysts including their kinetic, compositional, and morphological characteristics in methanol reforming reactions. Although research exploring surface reaction mechanism over various Cu-based catalysts was first attempted about three decades ago, the scheme remains controversial. This technical discussion will focus on the commonly reported surface intermediate species, which are methoxy, formaldehyde, dioxymethylene, formate and methyl formate. The surface reaction mechanism could be complicated by the introduction of reactants such as oxygen and steam, into the system as they would subsequently initiate secondary reactions. Different reaction schemes of methanol reforming are presented.
•All four systems of methanol reforming to produce hydrogen were considered.•Cu-based catalysts, Cu sintering and modifications were discussed.•Surface reaction mechanisms involving common intermediate species were outlined.•Reaction schemes were complicated by secondary reactions.
The successful modelling of the observed precipitation, a very important variable for a wide range of climate applications, continues to be one of the major challenges that climate scientists face ...today. When the Weather Research and Forecasting (WRF) model is used to dynamically downscale the Climate Forecast System Reanalysis (CFSR) over the Indo-Pacific region, with analysis (grid-point) nudging, it is found that the cumulus scheme used, Betts–Miller–Janjić (BMJ), produces excessive rainfall suggesting that it has to be modified for this region. Experimentation has shown that the cumulus precipitation is not very sensitive to changes in the cloud efficiency but varies greatly in response to modifications of the temperature and humidity reference profiles. A new version of the scheme, denoted "modified BMJ" scheme, where the humidity reference profile is more moist, was developed. In tropical belt simulations it was found to give a better estimate of the observed precipitation as given by the Tropical Rainfall Measuring Mission (TRMM) 3B42 data set than the default BMJ scheme for the whole tropics and both monsoon seasons. In fact, in some regions the model even outperforms CFSR. The advantage of modifying the BMJ scheme to produce better rainfall estimates lies in the final dynamical consistency of the rainfall with other dynamical and thermodynamical variables of the atmosphere.
Salinity stress affects global food producing areas by limiting both crop growth and yield. Attempts to develop salinity-tolerant rice varieties have had limited success due to the complexity of the ...salinity tolerance trait, high variation in the stress response and a lack of available donors for candidate genes for cultivated rice. As a result, finding suitable donors of genes and traits for salinity tolerance has become a major bottleneck in breeding for salinity tolerant crops. Twenty-two wild
relatives have been recognized as important genetic resources for quantitatively inherited traits such as resistance and/or tolerance to abiotic and biotic stresses. In this review, we discuss the challenges and opportunities of such an approach by critically analyzing evolutionary, ecological, genetic, and physiological aspects of
species. We argue that the strategy of rice breeding for better Na
exclusion employed for the last few decades has reached a plateau and cannot deliver any further improvement in salinity tolerance in this species. This calls for a paradigm shift in rice breeding and more efforts toward targeting mechanisms of the tissue tolerance and a better utilization of the potential of wild rice where such traits are already present. We summarize the differences in salinity stress adaptation amongst cultivated and wild
relatives and identify several key traits that should be targeted in future breeding programs. This includes: (1) efficient sequestration of Na
in mesophyll cell vacuoles, with a strong emphasis on control of tonoplast leak channels; (2) more efficient control of xylem ion loading; (3) efficient cytosolic K
retention in both root and leaf mesophyll cells; and (4) incorporating Na
sequestration in trichrome. We conclude that while amongst all wild relatives,
is arguably a best source of germplasm at the moment, genes and traits from the wild relatives,
,
, and
, should be targeted in future genetic programs to develop salt tolerant cultivated rice.
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