Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “relational databases”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,783 records · Page 99

Static Aerodynamics of the Mars Exploration Rover Entry Capsule

The static aerodynamics for the Mars Exploration Rover (MER) aeroshell are presented. This aerodynamic database was an integral part of the end-to-end simulation used in pre- entry analysis for determining the MER entry design requirements for development of the MER entry system, as well as targeting the MER landing sites. The database was constructed using the same approach used for Mars Pathfinder (MPF). However, the MER aerodynamic database is of much higher fidelity and tailored to the MER entry trajectories. This set of data includes direct simulation Monte Carlo calculations covering the transitional regime of the entry trajectory and computational fluid dynamics calculations describing the aerodynamics in the hypersonic and supersonic continuum regimes. An overview of the methodology used to generate the data is given along with comparisons to important features in the MPF aerodynamics and related heritage data. The MER and MPF comparison indicates that trajectory specific data is required to properly model the flight characteristics of a.blunt entry capsule at Mars.

Schoenenberger, Mark↗

Characterization of Potential Springs in the Lower Colorado Desert of Southern California using Satellite Radar and Landsat Time Series Analysis

Renewable energy development in southern California is receiving increasing attention due to potential impacts on wildlife habitats and water sources. This study was designed to quantify and map, for the first time, sub-surface water sources from springs in the Lower Colorado Desert area of southern California using satellite radar data and 30 years of Landsat satellite image data. Synthetic Aperture Radar (SAR) data was used to identify sub-surface water sources not already documented as springs or seeps in the National Hydrography Database. Landsat imagery starting in 1985 was used to characterize vegetation growth patterns at these suspected spring locations detected from SAR analysis. Results showed a total of 104 potential spring locations across the Lower Colorado Desert study area, 19 of which were detected within Solar Energy Zone (SEZ) development boundaries, roughly evenly split between Riverside East and Imperial East, and 13 of which (both inside and outside SEZs) showed relatively high green vegetation index values over the period of 1985 to 2015 that would depend on non-precipitation water sources associated with active springs.

DRECP↗

Determination of Lidar Ratio for Major Aerosol Types over Western North Pacific Based on Long-Term MPLNET Data

East Asia is the most complex region in the world for aerosol studies, as it encounters a lot of varieties of aerosols, and aerosol classification can be a challenge in this region. In the present study, we focused on the relationship between aerosol types and aerosol optical properties. We analyzed the long-term (2005–2012) data of vertical profiles of aerosol extinction coefficients, lidar ratio (Sp), and other aerosol optical properties obtained from a NASA Micro-Pulse Lidar Network and Aerosol Robotic Network site in northern Taiwan, which frequently receives Asian continental outflows. Based on aerosol extinction vertical profiles, the profiles were classified into two types: type 1 (single-layer structure) and type 2 (two-layer structure). Fall season (October–November) was the prevailing season for the Type 1, whereas type 2 mainly happened in spring (March–April). In type 1, air masses normally originated from three regional sectors, i.e., Asia continental (AC), Pacific Ocean (PO), and Southeast Asia (SA). The mean Sp values were 39 ± 17 sr, 30 ± 12 sr, and 38 ± 18 sr for the AC, PO, and SA sectors, respectively. The Sp results suggested that aerosols from the AC sector contained dust and anthropogenic particles, and aerosols from the PO sector were most likely sea salts. We further combined the EPA dust event database and backward trajectory analysis for type 2. Results showed that Sp was 41 ± 14 sr and 53 ± 21 sr for dust storm and biomass-burning events, respectively. The Sp for biomass-burning events in type 2 showed two peaks patterns. The first peak occurred within range of 30–50 sr corresponding to urban pollutant, and the second peak occurred within range of 60–80 sr in relation to biomass burning. Finally, our study summarized the Sp values for four major aerosol types over northern Taiwan, viz., urban (42 ± 18 sr), dust (34 ± 6 sr), biomass-burning (69 ± 12 sr), and oceanic (30 ± 12 sr). Our findings provide useful references for aerosol classification and air pollution identification over the western North Pacific.

Sheng-Hsiang Wang↗

Southern Great Plains Ice Nuclei Characterization Experiment Final Campaign Summary

The first ever ice nucleating particle (INP) measurements to be collected at the Southern Great Plains site were made during a period from late April to June 2014, as a trial for possible longer-term measurements at the site. These measurements will also be used to lay the foundation for understanding and parameterizing (for cloud resolving modeling) the sources of these climatically important aerosols as well as to augment the existing database containing this knowledge. Siting the measurements during the spring was intended to capture INP sources in or to this region from plant, soil, dust transported over long distances, biomass burning, and pollution aerosols at a time when they may influence warm-season convective clouds and precipitation. Data have been archived of real-time measurements of INP number concentrations as a function of processing conditions (temperature and relative humidity) during 18 days of sampling that spanned two distinctly different weather situations: a warm, dry and windy period with regional dust and biomass burning influences in early May, and a cooler period of frequent precipitation during early June. Precipitation delayed winter wheat harvesting, preventing intended sampling during that perturbation on atmospheric aerosols. INP concentrations were highest and most variable at all temperatures in the dry period, where we attribute the INP activity primarily to soil dust emissions. Additional offline INP analyses are underway to extend the characterization of INP to cover the entire mixed phase cloud regime from -5°C to -35°C during the full study. Initial comparisons between methods on four days show good agreement and excellent future promise. The additional offline immersion freezing data will be archived as soon as completed under separate funding. Analyses of additional specialized studies for specific attribution of INP to biological and smoke sources are continuing via the National Science Foundation and National Aeronautics and Space Administration funding that helped support instrumentation used for the measurements described herein. Aerosol Observing System aerosol data will be vital to the interpretation and parameterization of results as part of analyses for publications in preparation.

DeMott, PJ↗

Prophylaxis of Radiation-Induced Nausea and Vomiting Using 5-Hydroxytryptamine-3 Serotonin Receptor Antagonists: A Systematic Review of Randomized Trials

Purpose: To systematically review the effectiveness and safety of 5-hydroxytryptamine-3 receptor antagonists (5-HT3 RAs) compared with other antiemetic medication or placebo for prophylaxis of radiation-induced nausea and vomiting. Methods and Materials: We searched the following electronic databases: MEDLINE, Embase, the Cochrane Central Register of Controlled Clinical Trials, and Web of Science. We also hand-searched reference lists of included studies. Randomized, controlled trials that compared a 5-HT3 RA with another antiemetic medication or placebo for preventing radiation-induced nausea and vomiting were included. We excluded studies recruiting patients receiving concomitant chemotherapy. When appropriate, meta-analysis was conducted using Review Manager (v5) software. Relative risks were calculated using inverse variance as the statistical method under a random-effects model. We assessed the quality of evidence by outcome using the Grading of Recommendations Assessment, Development, and Evaluation approach. Results: Eligibility screening of 47 articles resulted in 9 included in the review. The overall methodologic quality was moderate. Meta-analysis of 5-HT3 RAs vs. placebo showed significant benefit for 5-HT3 RAs (relative risk [RR] 0.70; 95% confidence interval [CI] 0.57-0.86 for emesis; RR 0.84, 95% CI 0.73-0.96 for nausea). Meta-analysis comparing 5-HT3 RAs vs. metoclopramide showed a significant benefit of the 5-HT3 RAs for emetic control (RR 0.27, 95% CI 0.15-0.47). Conclusion: 5-Hydroxytryptamine-3 RAs are superior to placebo and other antiemetics for prevention of emesis, but little benefit was identified for nausea prevention. 5-Hydroxytryptamine-3 RAs are suggested for prevention of emesis. Limited evidence was found regarding delayed emesis, adverse events, quality of life, or need for rescue medication. Future randomized, controlled trials should evaluate different 5-HT3 antiemetics and new agents with novel mechanisms of action such at the NK{sub 1} receptor antagonists to determine the most effective drug. Delayed nausea and vomiting should be a focus of future study, perhaps concentrating on the palliative cancer population.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

Community transcriptomics reveals unexpected high microbial diversity in acidophilic biofilm communities

A fundamental question in microbial ecology relates to community structure, and how this varies across environment types. It is widely believed that some environments, such as those at very low pH, host simple communities based on the low number of taxa, possibly due to the extreme environmental conditions. However, most analyses of species richness have relied on methods that provide relatively low ribosomal RNA (rRNA) sampling depth. Here we used community transcriptomics to analyze the microbial diversity of natural acid mine drainage biofilms from the Richmond Mine at Iron Mountain, California. Our analyses target deep pools of rRNA gene transcripts recovered from both natural and laboratory-grown biofilms across varying developmental stages. In all, 91.8% of the ~ 254 million Illumina reads mapped to rRNA genes represented in the SILVA database. Up to 159 different taxa, including Bacteria, Archaea and Eukaryotes, were identified. Diversity measures, ordination and hierarchical clustering separate environmental from laboratory-grown biofilms. In part, this is due to the much larger number of rare members in the environmental biofilms. Although Leptospirillum bacteria generally dominate biofilms, we detect a wide variety of other Nitrospira organisms present at very low abundance. Bacteria from the Chloroflexi phylum were also detected. The results indicate that the primary characteristic that has enabled prior extensive cultivation-independent 'omic' analyses is not simplicity but rather the high dominance by a few taxa. Here, we conclude that a much larger variety of organisms than previously thought have adapted to this extreme environment, although only few are selected for at any one time.

59 BASIC BIOLOGICAL SCIENCES↗

The range of variation of the mass of the most massive star in stellar clusters derived from 35 million Monte Carlo simulations

A growing fraction of simple stellar population models, in an aim to create more realistic simulations capable of including stochastic variation in their outputs, begin their simulations with a distribution of discrete stars following a power-law function of masses. Careful attention is needed to create a correctly sampled initial mass function (IMF), and here we provide a solid mathematical method, called MASSCLEAN IMF Sampling, for doing so. We use our method to perform 10 million MASSCLEAN Monte Carlo stellar cluster simulations to determine the most massive star in a mass distribution as a function of the total mass of the cluster. We find that a maximum mass range is predicted, not a single maximum mass. This range is (1) dependent on the total mass of the cluster and (2) independent of an upper stellar mass limit, M{sub limit} , for unsaturated clusters and emerges naturally from our IMF sampling method. We then turn our analysis around, starting with our new database of 25 million simulated clusters, to constrain the highest mass star from the observed integrated colors of a sample of 40 low-mass Large Magellanic Cloud stellar clusters of known age and mass. Finally, we present an analytical description of the maximum mass range of the most massive star as a function of the cluster's total mass and present a new M{sub max} -M{sub cluster} relation.

79 ASTRONOMY AND ASTROPHYSICS↗

NEAR: Neural Embeddings for Amino acid Relationships

Protein language models (PLMs) have recently demonstrated potential to supplant classical protein database search methods based on sequence alignment, but are slower than common alignment-based tools and appear to be prone to a high rate of false labeling. Here, we present NEAR, a method based on neural representation learning that is designed to improve both speed and accuracy of search for likely homologs in a large protein sequence database. NEAR’s ResNet embedding model is trained using contrastive learning guided by trusted sequence alignments. It computes per-residue embeddings for target and query protein sequences, and identifies alignment candidates with a pipeline consisting of residue-level k-NN search and a simple neighbor aggregation scheme. Tests on a benchmark consisting of trusted remote homologs and randomly shuffled decoy sequences reveal that NEAR substantially improves accuracy relative to state-of-the-art PLMs, with lower memory requirements and faster embedding and search speed. While these results suggest that the NEAR model may be useful for standalone homology detection with increased sensitivity over standard alignment-based methods, in this manuscript we focus on a more straightforward analysis of the model’s value as a high-speed pre-filter for sensitive annotation. In that context, NEAR is at least 5x faster than the pre-filter currently used in the widely-used profile hidden Markov model (pHMM) search tool HMMER3, and also outperforms the pre-filter used in our fast pHMM tool, nail.

59 BASIC BIOLOGICAL SCIENCES↗

Next-Generation Marine Energy Software Needs Assessment

Over the past decade the marine energy industry has continued to grow and evolve, with new concepts and technologies constantly being pursued. Additionally, the field of computing is vastly different today than it was five or ten years ago. By utilizing advanced software and hardware architectures, like graphics processing units as well as parallelization and high-performance computing resources, software can produce higher quality outputs and a higher volume of outputs. These software and hardware resources can enable the marine energy community to exploit computational advancements from other research fields, which can include machine learning, differentiable programming, and controls co-design. Better integration of existing software and development of potential new software is necessary to take advantage of trends in modern computing and respond to the current and future needs of the marine energy community. In order to better understand the existing marine energy software landscape and industry needs, DOE's Water Power Technologies Office (WPTO) tasked Sandia National Laboratories and the National Renewable Energy Laboratory to update the needs assessment by identifying existing software gaps and software needs, and assisting WPTO in planning the next wave of marine energy software development. The proposed effort involved cataloguing and analyzing the available data on existing software related to marine energy. The marine energy software landscape has vastly changed in the last ten years. There are now nearly 230 different software packages utilized by the marine energy sector, compared to a decade ago when the Cardinal Engineering survey identified approximately 40 software packages. In 2012, the marine energy software landscape was captured in two tables, whereas the current marine energy software landscape required development of a software database to collect and categorize software.

16 TIDAL AND WAVE POWER↗

Cost of Fish Exclusion Technologies for Hydropower

This dataset compiles data on the costs of environmental mitigations related to fish exclusion for 35 hydropower projects based on documents obtained from the U.S Federal Energy Regulatory Commission (FERC). Data for all measures and their associated project-specific information, including installed and hydraulic capacities, were manually extracted from environmental impact statements and/or environmental assessment (EIS/EA) documents. All data were consolidated in a comma-separated (.csv) file, referred to as the Cost of Fish Exclusion Technologies for Hydropower Database.

13 HYDRO ENERGY↗

Land-use harmonization datasets for annual global carbon budgets

Abstract. Land-use change has been the dominant source of anthropogenic carbon emissions for most of the historical period and is currently one of the largest and most uncertain components of the global carbon cycle. Advancing the scientific understanding on this topic requires that the best data be used as input to state-of-the-art models in well-organized scientific assessments. The Land-Use Harmonization 2 dataset (LUH2), previously developed and used as input for simulations of the 6th Coupled Model Intercomparison Project (CMIP6), has been updated annually to provide required input to land models in the annual Global Carbon Budget (GCB) assessments. Here we discuss the methodology for producing these annual LUH2-GCB updates and extensions which incorporate annual wood harvest data updates from the Food and Agriculture Organization (FAO) of the United Nations for dataset years after 2015 and the History Database of the Global Environment (HYDE) gridded cropland and grazing area data updates (based on annual FAO cropland and grazing area data updates) for dataset years after 2012, along with extrapolations to the current year due to a lag of 1 or more years in the FAO data releases. The resulting updated LUH2-GCB datasets have provided global, annual gridded land-use and land-use-change data relating to agricultural expansion, deforestation, wood harvesting, shifting cultivation, regrowth and afforestation, crop rotations, and pasture management and are used by both bookkeeping models and dynamic global vegetation models (DGVMs) for the GCB. For GCB 2019, a more significant update to LUH2 was produced, LUH2-GCB2019 (https://doi.org/10.3334/ORNLDAAC/1851, Chini et al., 2020b), to take advantage of new data inputs that corrected cropland and grazing areas in the globally important region of Brazil as far back as 1950. From 1951 to 2012 the LUH2-GCB2019 dataset begins to diverge from the version of LUH2 used for the World Climate Research Programme's CMIP6, with peak differences in Brazil in the year 2000 for grazing land (difference of 100 000 km2) and in the year 2009 for cropland (difference of 77 000 km2), along with significant sub-national reorganization of agricultural land-use patterns within Brazil. The LUH2-GCB2019 dataset provides the base for future LUH2-GCB updates, including the recent LUH2-GCB2020 dataset, and presents a starting point for operationalizing the creation of these datasets to reduce time lags due to the multiple input dataset and model latencies.

Chini, Louise (ORCID:0000000290703505)↗

Expert system shell to reason on large amounts of data

The current data base management systems (DBMS's) do not provide a sophisticated environment to develop rule based expert systems applications. Some of the new DBMS's come with some sort of rule mechanism; these are active and deductive database systems. However, both of these are not featured enough to support full implementation based on rules. On the other hand, current expert system shells do not provide any link with external databases. That is, all the data are kept in the system working memory. Such working memory is maintained in main memory. For some applications the reduced size of the available working memory could represent a constraint for the development. Typically these are applications which require reasoning on huge amounts of data. All these data do not fit into the computer main memory. Moreover, in some cases these data can be already available in some database systems and continuously updated while the expert system is running. This paper proposes an architecture which employs knowledge discovering techniques to reduce the amount of data to be stored in the main memory; in this architecture a standard DBMS is coupled with a rule-based language. The data are stored into the DBMS. An interface between the two systems is responsible for inducing knowledge from the set of relations. Such induced knowledge is then transferred to the rule-based language working memory.

Giuffrida, Gionanni↗

High-Throughput Combinatorial Development of High-Entropy Alloys For Light-Weight Structural Applications

The primary limitation of today’s lightweight structural alloys is that specific yield strengths (SYS) higher than 200MPa x cc/g (typical value for titanium alloys) are extremely difficult to achieve. This holds true especially at a cost lower than 5dollars/kg (typical value for magnesium alloys). Recently, high-entropy alloys (HEA) have shown promising SYS, yet the large composition space of HEA makes screening compositions complex and time-consuming. Over the course of this 2-year project we started from 150 billion compositions and reduced the number of potential low-density (<5g/cc), low-cost (<5dollars/kg) high-entropy alloy (LDHEA) candidates that are single-phase, disordered, solid-solution (SPSS) to a few thousand compositions. This was accomplished by means of machine learning to guide design for SPSS LDHEA based on a combination of recursive partitioning, an extensive, experimental HEA database compiled from 24 literature sources, and 91 calculated parameters serving as phenomenological selection rules. Machine learning shows an accuracy of 82% in identifying which compositions of a separate, smaller, experimental HEA database are SPSS HEA. Calculation of Phase Diagrams (CALPHAD) shows an accuracy of 71-77% for the alloys supported by the CALPHAD database, where 30% of the compiled HEA database is not supported by CALPHAD. In addition to machine learning, and CALPHAD, a third tool was developed to aid design of SPSS LDHEA. Phase diagrams were calculated by constructing the Gibbs-free energy convex hull based on easily accessible enthalpy and entropy terms. Surprisingly, accuracy was 78%. Pursuing these LDHEA candidates by high-throughput experimental methods resulted in SPSS LDHEA composed of transition metals (e.g. Cr, Mn, Fe, Ni, Cu) alloyed with Al, yet the high concentration of Al, necessary to bring the mass density below 5.0g/cc, makes these materials hard and brittle, body-centered-cubic (BCC) alloys. A related, yet multi-phase BCC alloy, based on Al-Cr-Fe-Ni, shows compressive strain >10% and specific compressive yield strength of 229 MPa x cc/g, yet does not show ductility in tensile tests due to cleavage. When replacing Cr in Al-Cr-Fe-based 4- and 5-element LDHEA with Mn, hardness drops 2x. Combined with compression test results, including those on the ternaries Al-Cr-Fe and Al-Mn-Fe suggest that Al-Mn-Fe-based LDHEA are still worth pursuing. These initial results only represent one compressive stress-strain curve per composition without any property optimization. As such, reproducibility needs to be followed by optimization to show their full potential. When including Li, Mg, and Zn, single-phase Li-Mg-Al-Ti-Zn LDHEA has been found with a specific ultimate compressive strength of 289MPa x cc/g. Al-Ti-Mn-Zn showed a specific ultimate compressive strength of 73MPa x cc/g. These initial results after hot isostatic pressing (HIP) of the ball-milled powders represent the lower end of what is possible, since no secondary processing (e.g. extrusion) has been performed to optimize strength and ductility. Compositions for multi-phase (e.g. dual-phase) LDHEA were identified largely by automated searches through CALPHAD databases, while screening for large face-centered-cubic (FCC) volume fractions, followed by experimental verification. This resulted in several new alloys. Li-Mg-Al-Mn-Fe and Mg-Mn-Fe-Co ball-milled powders upon HIP show specific ultimate compressive strengths of 198MPa x cc/g and 45MPa x cc/g, respectively. Several malleable quarternary Al-Zn-based alloys have been found upon arc/induction melting, yet with limited specific compressive yield strength (<75 MPa x cc/g). These initial results are all without any optimization for strength and/or ductility. High-throughput experimentation allowed us to triple the existing experimental HEA database as published in the past 10 years in less than 2 years which happened at a rate 10x higher than previous methods. Furthermore, we showed that high-throughput thin-film combinatorial methods can be used to get insight in isothermal phase diagram slices. Although it is straightforward to map hardness as a function of composition for sputtered, thin-film, compositional gradients by nano-indentation and compare the results to micro-indentation on bulk samples, the simultaneous impact of composition, roughness, film density, and microstructure on hardness requires monitoring all these properties as a function of location on the compositional gradient, including dissecting the impact of these 4 factors on the hardness map. These additional efforts impact throughput significantly. This work shows that a lot of progress has been made over the years in predicting phase formation that aids the discovery of new alloys, yet that a lot of work needs to be done to predict phases more accurately for LDHEA, whether done by CALPHAD or by other means. More importantly, more work needs to be done to predict mechanical properties of novel alloys, like yield strength, and ductility. Furthermore, this work shows that there is a need for the generation of an empirical alloy database covering strategic points in a multi-dimensional composition space to allow for faster and more accurate predictive interpolations to identify the oasis in the dessert more quickly. Finally, this work suggests that it is worth pursuing a ductile alloy with a SYS > 300 MPa x cc/g in a mass density range of 6-7 g/cc, since the chances for a single-phase or majority-phase FCC increase significantly. Today’s lightweight steels are in this density range.

36 MATERIALS SCIENCE↗

Template for estimating uncertainties of measured neutron-induced fission cross-sections

A template for estimating uncertainties (unc.) of measured neutron-induced fission, (n,f), cross-sections (cs) is presented. This preliminary template not only lists all expected unc. sources but also supplies ranges of unc., estimates for correlations between unc. of the same and different experiments which can be used if the information is nonexistent. If this template is applied systematically when estimating experimental covariances for an evaluation, it may help in pinpointing missing unc. for individual datasets, identifying unreasonably low unc., and estimating correlations between different experimental datasets. Thus, a detailed unc. estimate – usually, a time-intensive procedure – can be undertaken more consistently and efficiently. As an example, it is shown that unc. and correlations of 239 Pu(n,f) by Merla et al. [Proceedings of the Conference on Nuclear Data for Science and Technology 1991 Jülich (Springer-Verlag, Berlin, 1992) pp. 510–513], which are questionably low in the GMA database underlying the neutron cs standards evaluations, are distinctly larger at 14.7 MeV and more strongly correlated if this template is used for reestimating the associated covariances.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Gradient-informed Hamiltonian Monte Carlo for multicomponent CALPHAD model optimization and uncertainty quantification

CALPHAD model parameter optimization is inherently challenging due to non-smooth objective functions, high-dimensional parameter spaces, and the need for uncertainty quantification (UQ). Traditional weighted nonlinear least squares approaches are computationally efficient but local, whereas black-box global optimizers and ensemble Markov Chain Monte Carlo (MCMC) methods provide broader exploration at substantial computational cost. The objective of this work is to combine the global exploration capability of gradient-informed Hamiltonian Monte Carlo – specifically the No-U-Turn Sampler (NUTS) – with local deterministic refinement using BFGS to efficiently optimize multicomponent CALPHAD models with minimal manual intervention. Analytic gradients are computed via the Jansson derivative framework. The methodology is demonstrated on the Cr—Fe binary system and extended to the Cr—Fe—Ni ternary system with 32 degrees of freedom. For Cr—Fe, NUTS achieves comparable or superior optimality relative to ensemble MCMC while requiring over an order-of-magnitude fewer likelihood evaluations. Parameter uncertainties are quantified through NUTS sampling and propagated to thermodynamic observables using local expansion, demonstrating a novel modular approach that combines binary and ternary parameter subsets without requiring global relaxation. These results establish gradient-informed exploration as a scalable strategy for multicomponent CALPHAD optimization and provide a practical route towards efficient higher-order database development with quantified uncertainty.

36 MATERIALS SCIENCE↗

Efficient computational design of two-dimensional van der Waals heterostructures: Band alignment, lattice mismatch, and machine learning

Here, we develop a computational database, website applications (web-apps), and machine-learning (ML) models to accelerate the design and discovery of two-dimensional (2D) heterostructures. Using density functional theory (DFT) based lattice parameters and electronic band energies for 674 nonmetallic exfoliable 2D materials, we generate 226 779 possible bilayer heterostructures. We classify these heterostructures into type-I, -II, and -III systems according to Anderson’s rule, which is based on the relative band alignments of the noninteracting monolayers. We find that type II is the most common and type III the least common heterostructure type. We subsequently analyze the chemical trends for each heterostructure type in terms of the Periodic Table of constituent elements. The band alignment data can also be used for identifying photocatalysts and high-work-function 2D metals for contacts. We validate our results by comparing them to experimental data as well as hybrid-functional predictions. Additionally, we carry out DFT calculations of a few selected systems (MoS 2 /WSe 2 , MoS 2 /h-BN, and MoSe 2 /CrI 3 ), to compare the band-alignment description with the predictions from Anderson’s rule. We develop web-apps to enable users to virtually create combinations of 2D materials and predict their properties. Additionally, we use ML tools to predict band-alignment information for 2D materials. The web-apps, tools, and associated data will be distributed through the JARVIS-HETEROSTRUCTURE website. Our analysis, results, and the developed web-apps can be applied to the screening and design applications, such as finding alternative photocatalysts, photodetectors, and high-work-function (WF) 2D-metal contacts.

2-dimensional systems↗

An invertible seven-dimensional Dirichlet cell characterization of lattices

Characterization of crystallographic lattices is an important tool in structure solution, crystallographic database searches and clustering of diffraction images in serial crystallography. Characterization of lattices by Niggli-reduced cells (based on the three shortest non-coplanar lattice vectors) or by Delaunay-reduced cells (based on four non-coplanar vectors summing to zero and all meeting at obtuse or right angles) is commonly performed. The Niggli cell derives from Minkowski reduction. The Delaunay cell derives from Selling reduction. All are related to the Wigner–Seitz (or Dirichlet, or Voronoi) cell of the lattice, which consists of the points at least as close to a chosen lattice point as they are to any other lattice point. The three non-coplanar lattice vectors chosen are here called the Niggli-reduced cell edges. Starting from a Niggli-reduced cell, the Dirichlet cell is characterized by the planes determined by 13 lattice half-edges: the midpoints of the three Niggli cell edges, the six Niggli cell face-diagonals and the four body-diagonals, but seven of the lengths are sufficient: three edge lengths, the three shorter of each pair of face-diagonal lengths, and the shortest body-diagonal length. These seven are sufficient to recover the Niggli-reduced cell.

Chemistry↗

SU-E-T-764: Track Repeating Algorithm for Proton Therapy Applied to Intensity Modulated Proton Therapy for Head-And-Neck Patients

Purpose: To determine the suitability of fast Monte Carlo techniques for dose calculation in particle therapy based on track-repeating algorithm for Intensity Modulated Proton Therapy, IMPT. The application of this technique will make possible detailed retrospective studies of large cohort of patients, which may lead to a better determination of Relative Biological Effects from the analysis of patient data. Methods: A cohort of six head-and-neck patients treated at the University of Texas MD Anderson Cancer Center with IMPT were utilized. The dose distributions were calculated with the standard Treatment Plan System, TPS, MCNPX, GEANT4 and FDC, a fast track-repeating algorithm for proton therapy for the verification and the patient plans. FDC is based on a GEANT4 database of trajectories of protons in a water. The obtained dose distributions were compared to each other utilizing the g-index criteria for 3mm-3% and 2mm-2%, for the maximum spatial and dose differences. The γ-index was calculated for voxels with a dose at least 10% of the maximum delivered dose. Dose Volume Histograms are also calculated for the various dose distributions. Results: Good agreement between GEANT4 and FDC is found with less than 1% of the voxels with a γ-index larger than 1 for 2 mm-2%. The agreement between MCNPX with FDC is within the requirements of clinical standards, even though it is slightly worse than the comparison with GEANT4.The comparison with TPS yielded larger differences, what is also to be expected because pencil beam algorithm do not always performed well in highly inhomogeneous areas like head-and-neck. Conclusion: The good agreement between a track-repeating algorithm and a full Monte Carlo for a large cohort of patients and a challenging, site like head-and-neck, opens the path to systematic and detailed studies of large cohorts, which may yield better understanding of biological effects.

60 APPLIED LIFE SCIENCES↗