Search

Multivariate metal-organic frameworks (MTV-MOFs) are characterized by their unique structural feature of incorporating multiple organic linkers and metal ions into a unified framework. This composite construction bestows MTV-MOFs with a broader spectrum of diverse and intricate properties compared to traditional single-component MOFs. In this study, the performance of 560 MTV-MOFs as hydrogen storage adsorbents has been thoroughly investigated. Firstly, the key structural parameters of materials, including density, pore occupied accessible volume, accessible surface area, and porosity were computed. Then, high-throughput Grand Canonical Monte Carlo (GCMC) simulations were employed to calculate hydrogen adsorption capacity of MTV-MOFs under hydrogen pressures of 100 bar at both 77 K and 298 K. Based on simulated results, the deliverable hydrogen capacity of these MTV-MOFs were deduced under both room temperature conditions (298 K, 97.2 bar → 298 K, 5 bar) and low-temperature conditions (77 K, 100 bar → 160 K, 5 bar). Through high-throughput screening, we identified top ten promising MTV-MOFs with the highest hydrogen storage capacity and conducted in-depth studies on their hydrogen adsorption properties. Furthermore, we developed a multivariate linear regression model to quantitatively predict the relationship between hydrogen adsorption capacity and their structural parameters for these MTV-MOFs. The predictions of this model align closely with the outcomes derived from GCMC simulations. The present study highlights the potential of MTV-MOFs as promising candidates for hydrogen storage applications, thereby providing valuable theoretical insights into the exploration of high-capacity hydrogen storage materials. • Structural parameters and H 2 adsorption properties of 560 MTV-MOFs are studied. • Structure-activity relationships are systematically studied by statistical analysis. • The top ten MTV-MOFs with the best H 2 storage performance are identified. • Machine learning method is used to develop a multivariate linear regression model. [ABSTRACT FROM AUTHOR]

The increasing use of multiple Workflow Management Systems (WMS) employing various workflow languages and shared workflow repositories enhances the open-source bioinformatics ecosystem. Efficient resource utilization in these systems is crucial for keeping costs low and improving processing times, especially for large-scale bioinformatics workflows running in cloud environments. Recognizing this, our study introduces a novel reference architecture, Cloud Monitoring Kit (CMK), for a multi-platform monitoring system. Our solution is designed to generate uniform, aggregated metrics from containerized workflow tasks scheduled by different WMS. Central to the proposed solution is the use of task labeling methods, which enable convenient grouping and aggregating of metrics independent of the WMS employed. This approach builds upon existing technology, providing additional benefits of modularity and capacity to seamlessly integrate with other data processing or collection systems. We have developed and released an open-source implementation of our system, which we evaluated on Amazon Web Services (AWS) using a transcriptomics data analysis workflow executed on two scientific WMS. The findings of this study indicate that CMK provides valuable insights into resource utilization. In doing so, it paves the way for more efficient management of resources in containerized scientific workflows running in public cloud environments, and it provides a foundation for optimizing task configurations, reducing costs, and enhancing scheduling decisions. Overall, our solution addresses the immediate needs of bioinformatics workflows and offers a scalable and adaptable framework for future advancements in cloud-based scientific computing. [ABSTRACT FROM AUTHOR]

Since the implementation of the Materials Genome Project by the Obama administration in the United States, the development of various computational materials' databases has fundamentally expanded the choice of industries such as materials and energy. In the field of thermoelectric materials, the thermoelectric figure of merit (ZT) quantifies the performance of the material. From the viewpoint of calculations for vast materials, the ZT values are not easily obtained due to their computational complexity. Here, we show how to build a database of thermoelectric materials based on first-principle calculations for the electronic and heat transport of materials. Firstly, the initial structures are classified according to the values of bandgap and other basic properties using the clustering algorithm K-means in machine learning, and high-throughput first principle calculations are carried out for narrow-bandgap semiconductors which exhibit a potential thermoelectric application. The present framework of calculations mainly includes a deformation potential module, an electrical transport performance module, a mechanical and a thermodynamic properties module. We have also set up a search webpage for the calculated database of thermoelectric materials, providing search facilities and the ability to view the related physical properties of materials. Our work may inspire the construction of more computational databases of first-principle thermoelectric materials and accelerate research progress in the field of thermoelectrics. [ABSTRACT FROM AUTHOR]

The dispersion of reinforced particles is crucial in improving the mechanical properties of ceramic particles reinforced Al matrix composites. In this work, a novel interfacial projection strategy was proposed to improve the dispersion of TiB2 particles in Al-Si composites through high-throughput first-principles calculations with the experimental verification. Firstly, the Al(111)/TiB2(0001) interface model was constructed. Then, the pre-substitution of Si at 2 at. % concentration was employed to simulate the solutionized Al matrix. Secondly, alloying behaviors of 55 elements (10 substitution sites per element) in the Si-Al(111)/TiB2(0001) interface were studied by high-throughput first-principles calculations in terms of the relative interface formation energy and the relative work of adhesion. With these values, a theoretical alloying map for evaluating the dispersion ability of these alloying elements was plotted. Accordingly, the copper mold experiments were conducted to validate the theoretical alloying map. Based on the electron analysis, the enhanced interface bonds by alloying primarily attributed to electron accumulations around Si atom, indicating that the pre-substitution Si atom was vital for a reliable simulation result. Generally, this interfacial projection strategy provides a precise and potent approach in designing high-performance Al matrix composites, and should be applicable to other reinforced particles.

High-throughput computing (HTC) is a computing paradigm that aims to accomplish jobs by easily breaking them into smaller, independent components. However, it requires a large amount of computing power for a long time. Most existing HTC frameworks are job-oriented without support for coscheduling with hardware architecture and task-level execution. Also, most of the frameworks reach a limited scale, and their usability needs further improvement. Herein, we present HTDcr, a job execution framework for the HTC on supercomputers. This study aims to improve the throughput, task dispatching, and usability of the framework. In detail, the throughput optimizations include a sophisticated designed task management system, a hierarchical scheduler, and the co-optimization of the task-scheduling strategy with the application and hardware characteristics. The optimizations for usability include a programable execution workflow, mechanisms for more robust and reliable service qualities, and a fine-grained resource allocation system for the colocation of multiple jobs. According to our evaluations, HTDcr can achieve outstanding scalability and high throughput on large-scale clusters for the HTC workload. We evaluate HTDcr with several microbenchmarks and real-world applications on Tianhe-2 and Sunway TaihuLight to demonstrate its effects on existing design mechanisms. For instance, the task scheduling for two real-world applications integrated with the application and hardware characteristics achieves 1.7× and 1.9× speedups over the basic task-scheduling strategy. [ABSTRACT FROM AUTHOR]

In this study, we performed a systematic search for dopants to stabilize the tetragonal structure of HfO2 and ZrO2 by using high-throughput first-principles calculations. By exhaustively exploring all possible impurity configurations for over 12,000 systems, we obtained the expected most stable doping status for various dopants at different doping levels. The stabilization effect of dopants are investigated and compared. The results show that Si or Ge significantly stabilize the tetragonal phase. [ABSTRACT FROM AUTHOR]

In today's world of big data, computational analysis has become a key driver of biomedical research. High-performance computational facilities are capable of processing considerable volumes of data, yet often lack an easy-to-use interface to guide the user in supervising and adjusting bioinformatics analysis via a tablet or smartphone. To address this gap we proposed Telescope, a novel tool that interfaces with high-performance computational clusters to deliver an intuitive user interface for controlling and monitoring bioinformatics analyses in real-time. By leveraging last generation technology now ubiquitous to most researchers (such as smartphones), Telescope delivers a friendly user experience and manages conectivity and encryption under the hood. Telescope helps to mitigate the digital divide between wet and computational laboratories in contemporary biology. By delivering convenience and ease of use through a user experience not relying on expertise with computational clusters, Telescope can help researchers close the feedback loop between bioinformatics and experimental work with minimal impact on the performance of computational tools. Telescope is freely available at https://github.com/Mangul-Lab-USC/telescope.

Recently, chalcogenide phase-change materials have been widely applied in phase-change random access memory. However, the materials still have shortcomings of poor stability and low crystalline resistivity, causing high-power consumption, resistance drift, and short device life in phase-change random access memory. These do not meet the technical requirements and need to be modified. To improve Sb-Te systems alloy materials' properties and discover new phase-change materials, in this work, we construct 16 solid-solution systems based on SbTe (Sb1−xNMx)Te and Sb(Te1−xNMx) (NM = noble metals). We use a high-throughput computing method to calculate and analyze the underlying physical mechanism of solid-solution noble metal atoms' effects on improving the performance of phase-change materials. Based on the calculation results, we believe that the (Sb1−xNMx)Te solid solutions are more stable than Sb(Te1−xNMx). At the same time, the solid solution of the substituted Sb atom sites keeps the crystal structure symmetry improved structural stability. Furthermore, lone-pair electrons exist due to (Sb1−xNMx)Te keeping the SbTe's unique layer structure, which confers a higher activity of the surrounding atoms. This is an essential determinant for keeping the phase-change properties. On the other hand, (Sb1−xNMx)Te solid solutions increase the band gap, leading to increased resistivity. Considering the structural stability and electrical properties, we believe that the (Sb1−xRux) and (Sb1−xPdx)Te systems can create new phase-change materials. [ABSTRACT FROM AUTHOR]

A methodology to thermo-mechanically optimize a piston thermal barrier coating over a full drive cycle was established. The optimization objective was to minimize the heat transfer to the engine wall while maintaining structural integrity of the coating. Over 800 candidate materials were investigated and the optimization required more than one million non-road transient drive cycle calculations; real materials were investigated to ensure a realizable result and the existence of thermal and mechanical properties. High computational efficiency was achieved using a recently developed analytical heat transfer technique for multilayer engine walls. An uncoupled approach was utilized for the optimization, wherein the gas temperature and heat transfer coefficient profiles from a fully coupled and calibrated baseline model over the 20-min drive cycle were employed. The coating/piston interface temperature was constrained to be below the maximum piston service temperature limit. The durability was assessed using a recently developed analytical coating delamination framework for engine in-cylinder coatings based on the energy release rate when a crack forms. Results are presented for a mechanically unconstrained optimization and for cases constrained to three fixed levels of drive-cycle maximum energy release rate, and also constrained by the individual material's toughness. The best-performing coating materials identified were verified using the fully coupled system-level model, which compared well to the uncoupled predictions. A study on the effect of adding a sealing layer to some high-performing, but porous, coatings showed a reduction in fuel consumption benefit and an increased exhaust temperature over the cycle, but the system still outperformed the uncoated case. The results of the study elucidate the importance of including engine performance and mechanical failure considerations in thermal barrier coating design. [ABSTRACT FROM AUTHOR]

The conversion and utilization of carbon dioxide (CO2) have dual significance for reducing carbon emissions and solving energy demand. Catalytic reduction of CO2 is a promising way to convert and utilize CO2. However, high-performance catalysts with excellent catalytic activity, selectivity and stability are currently lacking. High-throughput methods offer an effective way to screen high-performance CO2 reduction catalysts. Here, recent advances in high-throughput screening of electrocatalysts for CO2 reduction are reviewed. First, the mechanism of CO2 reduction reaction by electrocatalysis and potential catalyst candidates are introduced. Second, high-throughput computational methods developed to accelerate catalyst screening are presented, such as density functional theory and machine learning. Then, high-throughput experimental methods are outlined, including experimental design, high-throughput synthesis, in situ characterization and high-throughput testing. Finally, future directions of high-throughput screening of CO2 reduction electrocatalysts are outlooked. This review will be a valuable reference for future research on high-throughput screening of CO2 electrocatalysts. [ABSTRACT FROM AUTHOR]

It is of great academic and engineering application to study the evolution of microstructure and properties of age-strengthened aluminum alloys during heat treatment and to establish quantitative prediction models that can be applied to industrial production. The main factors affecting the peak aging state strength of age-strengthened aluminum alloys are the precipitates, solid solution elements, grain size effects, and textures formed during the material processing. In this work, these multi-scale factors are integrated into the framework of the knowledge graph to assist the following crystal plasticity finite elements simulations. The constructed knowledge graph is divided into two parts: static data and dynamic data. Static data contains the basic properties of the material and the most basic property parameters. Dynamic data is designed to improve awareness of static data. High-throughput computing is performed to further obtain clear microstructure-property relationships by varying the parameters of materials properties and the characteristics of the structure models. The constructed knowledge graph can be used to guide material design for 6XXX Al-Mg-Si based alloys. The past experimental values are used to calibrate the phenomenological parameters and test the reliability of the analysis process. [ABSTRACT FROM AUTHOR]

[Display omitted] • A novel interfacial projection strategy was proposed to improve TiB 2 dispersion. • A high-throughput first-principles computing algorithm was proposed. • A theoretical alloying map with 55 alloying elements was established. • Rapid solidification experiments were conducted to validate alloying map successfully. • Pre-substitution Si atom was decisive for simulating Al-Si composites. The dispersion of reinforced particles is crucial in improving the mechanical properties of ceramic particles reinforced Al matrix composites. In this work, a novel interfacial projection strategy was proposed to improve the dispersion of TiB 2 particles in Al-Si composites through high-throughput first-principles calculations with the experimental verification. Firstly, the Al(1 1 1)/TiB 2 (0001) interface model was constructed. Then, the pre-substitution of Si at 2 at. % concentration was employed to simulate the solutionized Al matrix. Secondly, alloying behaviors of 55 elements (10 substitution sites per element) in the Si-Al(1 1 1)/TiB 2 (0001) interface were studied by high-throughput first-principles calculations in terms of the relative interface formation energy and the relative work of adhesion. With these values, a theoretical alloying map for evaluating the dispersion ability of these alloying elements was plotted. Accordingly, the copper mold experiments were conducted to validate the theoretical alloying map. Based on the electron analysis, the enhanced interface bonds by alloying primarily attributed to electron accumulations around Si atom, indicating that the pre-substitution Si atom was vital for a reliable simulation result. Generally, this interfacial projection strategy provides a precise and potent approach in designing high-performance Al matrix composites, and should be applicable to other reinforced particles. [ABSTRACT FROM AUTHOR]

Scientific applications often require substantial amount of computing resources for running challenging jobs potentially consisting of many tasks from hundreds of thousands to even millions. As a result, many institutions collaborate to solve large-scale problems by creating virtual organizations (VOs), and integrate hundreds of thousands of geographically distributed heterogeneous computing resources. Over the past decade, VOs have been proven to be a powerful research testbed for accessing massive amount of computing resources shared by several organizations at almost no cost. However, VOs often suffer from providing exact dynamic resource information due to their scale and autonomous resource management policies. Furthermore, shared resources are inconsistent, making it difficult to accurately forecast resource capacity. An effective VO's resource profiling and modeling system can address these problems by forecasting resource characteristics and availability. This paper presents effective resource profiling and performance prediction models including Adaptive Filter-based Online Linear Regression (AFOLR) and Adaptive Filter-based Moving Average (AFMV) based on the linear difference equation combining past predicted values and recent profiled information, which aim to support large-scale applications in distributed scientific computing environments. We performed quantitative analysis and conducted microbenchmark experiments on a real multinational shared computing platform. Our evaluation results demonstrate that the proposed prediction schemes outperform well-known common approaches in terms of accuracy, and actually can help users in a shared resource environment to run their large-scale applications by effectively forecasting various computing resource capacity and performance. [ABSTRACT FROM AUTHOR]

This paper introduces FireWorks, a workflow software for running high-throughput calculation workflows at supercomputing centers. FireWorks has been used to complete over 50 million CPU-hours worth of computational chemistry and materials science calculations at the National Energy Research Supercomputing Center. It has been designed to serve the demanding high-throughput computing needs of these applications, with extensive support for (i) concurrent execution through job packing, (ii) failure detection and correction, (iii) provenance and reporting for long-running projects, (iv) automated duplicate detection, and (v) dynamic workflows (i.e., modifying the workflow graph during runtime). We have found that these features are highly relevant to enabling modern data-driven and high-throughput science applications, and we discuss our implementation strategy that rests on Python and NoSQL databases (MongoDB). Finally, we present performance data and limitations of our approach along with planned future work.

This paper introduces FireWorks, a workflow software for running high-throughput calculation workflows at supercomputing centers. FireWorks has been used to complete over 50 million CPU-hours worth of computational chemistry and materials science calculations at the National Energy Research Supercomputing Center. It has been designed to serve the demanding high-throughput computing needs of these applications, with extensive support for (i) concurrent execution through job packing, (ii) failure detection and correction, (iii) provenance and reporting for long-running projects, (iv) automated duplicate detection, and (v) dynamic workflows (i.e., modifying the workflow graph during runtime). We have found that these features are highly relevant to enabling modern data-driven and high-throughput science applications, and we discuss our implementation strategy that rests on Python and NoSQL databases (MongoDB). Finally, we present performance data and limitations of our approach along with planned future work.

The inverse heat transfer problems (IHTP) have a wide range of applications in the engineering field. Bayesian methods using Markov Chain Monte Carlo (MCMC) have long been considered as a robust and effective method for solving inverse problems. However, the discretization of the problem domain by the spatio-temporal Galerkin skill, i.e., the finite element interpolation also includes the time dimension, making the scale of the unknown parameters extremely difficult for Bayesian calculations. In this paper, a fast Bayesian parallel sampling (FBPS) framework is proposed for large-scale parameter estimation of benchmark three-dimensional inverse heat transfer problems (3D-IHTP). The FBPS we developed achieves a parameter computation scale of 10 5 magnitude within minutes, through dimensionality reduction of the space-time dependent problem domain. The Hamiltonian Monte Carlo (HMC) sampler, which is proven to be more efficient for high-dimensional parameter estimation, is employed. Through several simulation tests of IHTP, it was confirmed that the solving efficiency of FBPS surpasses that of the traditional Bayesian strategy significantly. Finally, FBPS is successfully developed to estimate the unknown heat flux on the chip heat sink and pack interface effectively, given some simulated high resolution measurement data. The reliability and efficiency show that FBPS has the potential to support efficient prediction techniques for a class of IHTPs in engineering applications. • A fast Bayesian parallel sampling framework (FBPS) for 3D-IHTP is proposed. • Computation time to solve IHTP with unknown size of 105 is in minute level. • FBPS is about 55 times faster than the traditional Gibbs method. • FBPS provides reliable technical idea to solving large-scale Bayesian inverse problem. • FBPS is applied to the inverse problem of chip packaging heat dissipation. [ABSTRACT FROM AUTHOR]

The transverse strength of unidirectional carbon fiber reinforced polymer (UD-CFRP) composites is a high dimensional and nonlinear function of microstructure due to the wide scatter in mechanical properties and complex failure mechanisms, which is a challenging task to develop a general microstructure dependent strength criterion (MDSC) in theory or computation. Volume fraction and distribution of fibers are among the crucial influencing factors. A computational micromechanics and machine learning (ML) combined method is employed to uncover the transverse mechanical response of UD-CFRP composites. High-throughput finite element analyses (FEA) are performed to obtain the transverse behaviors of composites with varying fiber distribution and volume fraction under different loading states. They showed that fiber distribution has different effects on strengths in different failure modes, while the failure modes are closely related to loading states and fiber volume fractions. An ML model is then trained to characterize the relations between composite microstructure and composite strength. Then, the transverse strengths of 1000 new microstructures are predicted, which shows good agreement with FEA results, so that the MDSC of UD-CFRP is constructed by fully accounting for the influence of fiber distribution. Reliability of the method is verified by considering composites with various fiber volume fractions. [Display omitted] • High-throughput finite element analyses are performed to uncover the transverse mechanical response of UD-CFRP composites. • The transverse behaviors of composites with various fiber distribution and fiber volume fraction are presented. • The complex relations between microstructure of composites and transverse strengths are represented. • The microstructure dependent transverse strength criterion is constructed by considering the effect of fiber distribution. [ABSTRACT FROM AUTHOR]

The conversion and utilization of carbon dioxide (CO2) have dual significance for reducing carbon emissions and solving energy demand. Catalytic reduction of CO2 is a promising way to convert and utilize CO2. However, high-performance catalysts with excellent catalytic activity, selectivity and stability are currently lacking. High-throughput methods offer an effective way to screen high-performance CO2 reduction catalysts. Here, recent advances in high-throughput screening of electrocatalysts for CO2 reduction are reviewed. First, the mechanism of CO2 reduction reaction by electrocatalysis and potential catalyst candidates are introduced. Second, high-throughput computational methods developed to accelerate catalyst screening are presented, such as density functional theory and machine learning. Then, high-throughput experimental methods are outlined, including experimental design, high-throughput synthesis, in situ characterization and high-throughput testing. Finally, future directions of high-throughput screening of CO2 reduction electrocatalysts are outlooked. This review will be a valuable reference for future research on high-throughput screening of CO2 electrocatalysts.

Whole Genome Sequence (WGS) data from bacterial species is used for a variety of applications ranging from basic microbiological research, diagnostics, and epidemiological surveillance. The availability of WGS data from hundreds of thousands of individual isolates of individual microbial species poses a tremendous opportunity for discovery and hypothesis-generating research into ecology and evolution of these microorganisms. Flexibility, scalability, and user-friendliness of existing pipelines for population-scale inquiry, however, limit applications of systematic, population-scale approaches. Here, we present ProkEvo, an automated, scalable, reproducible, and open-source framework for bacterial population genomics analyses using WGS data. ProkEvo was specifically developed to achieve the following goals: (1) Automation and scaling of complex combinations of computational analyses for many thousands of bacterial genomes from inputs of raw Illumina paired-end sequence reads; (2) Use of workflow management systems (WMS) such as Pegasus WMS to ensure reproducibility, scalability, modularity, fault-tolerance, and robust file management throughout the process; (3) Use of high-performance and high-throughput computational platforms; (4) Generation of hierarchical-based population structure analysis based on combinations of multi-locus and Bayesian statistical approaches for classification for ecological and epidemiological inquiries; (5) Association of antimicrobial resistance (AMR) genes, putative virulence factors, and plasmids from curated databases with the hierarchically-related genotypic classifications; and (6) Production of pan-genome annotations and data compilation that can be utilized for downstream analysis such as identification of population-specific genomic signatures. The scalability of ProkEvo was measured with two datasets comprising significantly different numbers of input genomes (one with ~2,400 genomes, and the second with ~23,000 genomes). Depending on the dataset and the computational platform used, the running time of ProkEvo varied from ~3-26 days. ProkEvo can be used with virtually any bacterial species, and the Pegasus WMS uniquely facilitates addition or removal of programs from the workflow or modification of options within them. To demonstrate versatility of the ProkEvo platform, we performed a hierarchical-based population structure analyses from available genomes of three distinct pathogenic bacterial species as individual case studies. The specific case studies illustrate how hierarchical analyses of population structures, genotype frequencies, and distribution of specific gene functions can be integrated into an analysis. Collectively, our study shows that ProkEvo presents a practical viable option for scalable, automated analyses of bacterial populations with direct applications for basic microbiology research, clinical microbiological diagnostics, and epidemiological surveillance.

Whole Genome Sequence (WGS) data from bacterial species is used for a variety of applications ranging from basic microbiological research, diagnostics, and epidemiological surveillance. The availability of WGS data from hundreds of thousands of individual isolates of individual microbial species poses a tremendous opportunity for discovery and hypothesis-generating research into ecology and evolution of these microorganisms. Flexibility, scalability, and user-friendliness of existing pipelines for population-scale inquiry, however, limit applications of systematic, population-scale approaches. Here, we present ProkEvo, an automated, scalable, reproducible, and open-source framework for bacterial population genomics analyses using WGS data. ProkEvo was specifically developed to achieve the following goals: (1) Automation and scaling of complex combinations of computational analyses for many thousands of bacterial genomes from inputs of raw Illumina paired-end sequence reads; (2) Use of workflow management systems (WMS) such as Pegasus WMS to ensure reproducibility, scalability, modularity, fault-tolerance, and robust file management throughout the process; (3) Use of high-performance and high-throughput computational platforms; (4) Generation of hierarchical-based population structure analysis based on combinations of multi-locus and Bayesian statistical approaches for classification for ecological and epidemiological inquiries; (5) Association of antimicrobial resistance (AMR) genes, putative virulence factors, and plasmids from curated databases with the hierarchically-related genotypic classifications; and (6) Production of pan-genome annotations and data compilation that can be utilized for downstream analysis such as identification of population-specific genomic signatures. The scalability of ProkEvo was measured with two datasets comprising significantly different numbers of input genomes (one with ~2,400 genomes, and the second with ~23,000 genomes). Depending on the dataset and the computational platform used, the running time of ProkEvo varied from ~3-26 days. ProkEvo can be used with virtually any bacterial species, and the Pegasus WMS uniquely facilitates addition or removal of programs from the workflow or modification of options within them. To demonstrate versatility of the ProkEvo platform, we performed a hierarchical-based population structure analyses from available genomes of three distinct pathogenic bacterial species as individual case studies. The specific case studies illustrate how hierarchical analyses of population structures, genotype frequencies, and distribution of specific gene functions can be integrated into an analysis. Collectively, our study shows that ProkEvo presents a practical viable option for scalable, automated analyses of bacterial populations with direct applications for basic microbiology research, clinical microbiological diagnostics, and epidemiological surveillance. [ABSTRACT FROM AUTHOR]

To be an effective tool in studying cardiovascular disease and designing new treatments, computational fluid dynamics (CFD) needs to move from the realm of "high performance" to "high throughput" computing by improving efficiency while retaining accuracy. Solution adaptive mesh refinement (AMR) has the potential to decrease simulation time and benefits would be greater if the mesh could dynamically adapt throughout a heartbeat. This study used cut‐cell Cartesian grids with AMR at each time step. The refinement criteria was based on subgrid scale (SGS). The potential efficiency improvements from AMR were investigated with the FDA nozzle benchmark case at Re 500, 3500 and 6500. Using AMR with SGS = 1% mean throat velocity simulations could be performed in under 24 h on 16 CPUs with high accuracy compared to both FDA particle image velocimetry experiments and refined uniform grid CFD. This was an efficiency improvement of over an order of magnitude compared to uniform grids and other AMR SGS values. Using AMR with SGS = 5 or 0.2% mean throat velocity did not result in large efficiency improvements. Lastly, simulations of pulsatile flow suggest that performance improvements for typical cardiovascular flows may be even greater than were found simulating the statistically stationary FDA nozzle experiments. [ABSTRACT FROM AUTHOR]

The Molecular Sciences Software Institute's (MolSSI) Quantum Chemistry Archive (QCArchive) project is an umbrella name that covers both a central server hosted by MolSSI for community data and the Python‐based software infrastructure that powers automated computation and storage of quantum chemistry (QC) results. The MolSSI‐hosted central server provides the computational molecular sciences community a location to freely access tens of millions of QC computations for machine learning, methodology assessment, force‐field fitting, and more through a Python interface. Facile, user‐friendly mining of the centrally archived quantum chemical data also can be achieved through web applications found at https://qcarchive.molssi.org. The software infrastructure can be used as a standalone platform to compute, structure, and distribute hundreds of millions of QC computations for individuals or groups of researchers at any scale. The QCArchive Infrastructure is open‐source (BSD‐3C), code repositories can be found at https://github.com/MolSSI, and releases can be downloaded via PyPI and Conda. This article is categorized under:Electronic Structure Theory > Ab Initio Electronic Structure MethodsSoftware > Quantum ChemistryData Science > Computer Algorithms and Programming [ABSTRACT FROM AUTHOR]

Controlling the magnetic state of two-dimensional (2D) materials is crucial for spintronics. By employing data-mining and autonomous density functional theory calculations, we demonstrate the switching of magnetic properties of 2D non-van der Waals materials upon hydrogen passivation. The magnetic configurations are tuned to states with flipped and enhanced moments. For 2D CdTiO 3 ─a diamagnetic compound in the pristine case─we observe an onset of ferromagnetism upon hydrogenation. Further investigation of the magnetization density of the pristine and passivated systems provides a detailed analysis of modified local spin symmetries and the emergence of ferromagnetism. Our results indicate that selective surface passivation is a powerful tool for tailoring magnetic properties of nanomaterials, such as non-vdW 2D compounds.

This paper is on the optimization of computing resources to process geospatial image data in a cloud computing infrastructure. Parallelization was tested by combining two different strategies: image tiling and multi-threading. The objective here was to get insight on the optimal use of available processing resources in order to minimize the processing time. Maximum speedup was obtained when combining tiling and multi-threading techniques. Both techniques are complementary, but a trade-off also exists. Speedup is improved with tiling, as parts of the image can run in parallel. But reading part of the image introduces an overhead and increases the relative part of the program that can only run in serial. This limits speedup that can be achieved via multi-threading. The optimal strategy of tiling and multi-threading that maximizes speedup depends on the scale of the application (global or local processing area), the implementation of the algorithm (processing libraries), and on the available computing resources (amount of memory and cores). A medium-sized virtual server that has been obtained from a cloud service provider has rather limited computing resources. Tiling will not only improve speedup but can be necessary to reduce the memory footprint. However, a tiling scheme with many small tiles increases overhead and can introduce extra latency due to queued tiles that are waiting to be processed. In a high-throughput computing cluster with hundreds of physical processing cores, more tiles can be processed in parallel, and the optimal strategy will be different. A quantitative assessment of the speedup was performed in this study, based on a number of experiments for different computing environments. The potential and limitations of parallel processing by tiling and multi-threading were hereby assessed. Experiments were based on an implementation that relies on an application programming interface (API) abstracting any platform-specific details, such as those related to data access.

Summary: This paper presents feasibility studies in utilizing graphics processing units (GPUs) as high‐performance computing hardware with front‐end electronics in high‐scale magnetic confinement thermal fusion experiments. The objective of the research is to provide scalable, high‐throughput, and low‐latency measurements for the runtime tokamak metallic impurities X‐ray diagnostic for the Tungsten Environment in Steady‐State Tokamak (WEST) reactor. The heterogeneous system of front‐end with field‐programmable gate arrays and the back‐end server was introduced to decompose workloads efficiently. It allows the comprehensive evaluation of CPUs and accelerators. In particular, a novel implementation of the back‐end algorithm for GPU with the performance analysis are presented. [ABSTRACT FROM AUTHOR]

"Volunteer computing" is the use of consumer digital devices for high-throughput scientific computing. It can provide large computing capacity at low cost, but presents challenges due to device heterogeneity, unreliability, and churn. BOINC, a widely-used open-source middleware system for volunteer computing, addresses these challenges. We describe BOINC's features, architecture, implementation, and algorithms. [ABSTRACT FROM AUTHOR]

Volunteer computing uses millions of consumer computing devices (desktop and laptop computers, tablets, phones, appliances, and cars) to do high-throughput scientific computing. It can provide Exa-scale capacity, and it is a scalable and sustainable alternative to data-center computing. Currently, about 30 science projects use volunteer computing in areas ranging from biomedicine to cosmology. Each project has application programs with particular hardware and software requirements (memory, GPUs, VM support, and so on). Each volunteered device has specific hardware and software capabilities, and each device owner has preferences for which science areas they want to support. This leads to a scheduling problem: how to dynamically assign devices to projects in a way that satisfies various constraints and that balances various goals. We describe the scheduling policy used in Science United, a global manager for volunteer computing.

The discovery of new carbon allotropes with different building blocks and crystal symmetries has long been of great interest to broad materials science fields. Herein, we report several hundred new carbon allotropes predicted by the state-of-the-art RG2 code and first-principles calculations. The types of new carbon allotropes that were identified in this work span pure sp2, hybrid sp2/sp3, and pure sp3 C–C bonding. All structures were globally optimized at the first-principles level. The thermodynamic stability of some selected carbon allotropes was further validated by computing their phonon dispersions. The predicted carbon allotropes possess a broad range of Vickers’ hardness. This wide range of Vickers’ hardness is explained in detail in terms of both atomic descriptors such as density, volume per atom, packing fraction, and local potential energy throughout the unit cell, and global descriptors such as elastic modulus, shear modulus, and bulk modulus, universal anisotropy, Pugh’s ratio, and Poisson’s ratio. For the first time, we found strong correlation between Vickers’ hardness and average local potentials in the unit cell. This work provides deep insight into the identification of novel carbon materials with high Vickers’ hardness for modern applications in which ultrahigh hardness is desired. Moreover, the local potential averaged over the entire unit cell of an atomic structure, an easy-to-evaluate atomic descriptor, could serve as a new atomic descriptor for efficient screening of the mechanical properties of unexplored structures in future high-throughput computing and artificial-intelligence-accelerated materials discovery methods.

We survey the areas of Desktop Grid task scheduling that seem to be insufficiently studied so far and are promising for efficiency, reliability, and quality of Desktop Grid computing. These topics include optimal task grouping, “needle in a haystack” paradigm, game-theoretical scheduling, domain-imposed approaches, special optimization of the final stage of the batch computation, and Enterprise Desktop Grids.

Background: Safety issues are detected in about one third of prescription drugs in the years following regulatory agency approval. Older adults, especially those with chronic kidney disease, are at particular risk of adverse reactions to prescription drugs. This protocol describes a new approach that may identify credible drug-safety signals more efficiently using administrative health care data., Objective: To use high-throughput computing and automation to conduct 700+ drug-safety cohort studies in older adults in Ontario, Canada. Each study will compare 74 acute (30-day) outcomes in patients who start a new prescription drug (new users) to a group of nonusers with similar baseline health characteristics. Risks will be assessed within strata of baseline kidney function., Design and Setting: The studies will be population-based, new-user cohort studies conducted using linked administrative health care databases in Ontario, Canada (January 1, 2008, to March 1, 2020). The source population for these studies will be residents of Ontario aged 66 years or older who filled at least one outpatient prescription through the Ontario Drug Benefit (ODB) program during the study period (all residents have universal health care, and those aged 65+ have universal prescription drug coverage through the ODB)., Patients: We identified 3.2 million older adults in the source population during the study period and built 700+ initial medication cohorts, each containing mutually exclusive groups of new users and nonusers. Nonusers were randomly assigned cohort entry dates that followed the same distribution of prescription start dates as new users. Eligibility criteria included a baseline estimated glomerular filtration rate (eGFR) measurement within 12 months before the cohort entry date (median time was 71 days before cohort entry in the new user group), no prior receipt of maintenance dialysis or a kidney transplant, and no prior prescriptions for drugs in the same subclass as the study drug. New users and nonusers will be balanced on ~400 baseline health characteristics using inverse probability of treatment weighting on propensity scores within 3 strata of baseline eGFR: ≥60, 45 to <60, <45 mL/min per 1.73 m 2 ., Outcomes: We will compare new user and nonuser groups on 74 clinically relevant outcomes (17 composites and 57 individual outcomes) in the 30 days after cohort entry. We used a prespecified approach to identify these 74 outcomes., Statistical Analysis Plan: In each cohort, we will obtain eGFR-stratum-specific weighted risk ratios and risk differences using modified Poisson regression and binomial regression, respectively. Additive and multiplicative interaction by eGFR category will be examined. Drug-outcome associations that meet prespecified criteria (identified signals) will be further examined in additional analyses (including survival, negative-control exposure, and E-value analyses) and visualizations., Results: The initial medication cohorts had a median of 6120 new users per cohort (interquartile range: 1469-38 839) and a median of 1 088 301 nonusers (interquartile range: 751 697-1 267 009). Medications with the largest number of new users were amoxicillin trihydrate (n = 1 000 032), cephalexin (n = 571 566), prescription acetaminophen (n = 571 563), and ciprofloxacin (n = 504,374); 19% to 29% of new users in these cohorts had an eGFR <60 mL/min per 1.73 m 2 ., Limitations: Despite our use of robust techniques to balance baseline indicators and to control for confounding by indication, residual confounding will remain a possibility. Only acute (30-day) outcomes will be examined. Our data sources do not include nonprescription (over-the-counter) drugs or drugs prescribed in hospitals and do not include outpatient prescription drug use in children or adults <65 years., Conclusion: This accelerated approach to conducting postmarket drug-safety studies has the potential to more efficiently detect drug-safety signals in a vulnerable population. The results of this protocol may ultimately help improve medication safety., Competing Interests: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article., (© The Author(s) 2024.)

Several studies have proved the benefits of job malleability, that is, the capacity of an application to adapt its parallelism to a dynamically changing number of allocated processors. The most remarkable advantages of executing malleable jobs as part of a high performance computer workload are the throughput increase and the more efficient utilization of the underlying resources. Malleability has been mostly applied to iterative applications where all the processes execute the same operations over different sets of data and with a balanced per process load. Unfortunately, not all scientific applications adhere to this process-level malleable job structure. There are scientific applications which are either noniterative or present an irregular per process load distribution. Unlike many other reconfiguration tools, the Dynamic Management of Resources Application Programming Interface (DMR API) provides the necessary flexibility to make malleable these out-of-target applications. In this article, we study the particular case of using the DMR API to generate a malleable version of HPG aligner, a distributed-memory noniterative genomic sequencer featuring an irregular communication pattern among processes. Through this first conversion of an out-of-target application to a malleable job, we both illustrate how the DMR API may be used to convert this type of applications into malleable and test the benefits of this conversion in production clusters. Our experimental results reveal an important reduction of the malleable HPG aligner jobs completion time compared to the original HPG aligner version. Furthermore, HPG aligner malleable workloads achieve a greater throughput than their fixed counterparts. [ABSTRACT FROM AUTHOR]

The use of high-fidelity computational simulations promises to enable high-throughput hypothesis testing and optimisation of cancer therapies. However, increasing realism comes at the cost of increasing computational requirements. This article explores the use of surrogate-assisted evolutionary algorithms to optimise the targeted delivery of a therapeutic compound to cancerous tumour cells with the multicellular simulator, PhysiCell. The use of both Gaussian process models and multi-layer perceptron neural network surrogate models are investigated. We find that evolutionary algorithms are able to effectively explore the parameter space of biophysical properties within the agent-based simulations, minimising the resulting number of cancerous cells after a period of simulated treatment. Both model-assisted algorithms are found to outperform a standard evolutionary algorithm, demonstrating their ability to perform a more effective search within the very small evaluation budget. This represents the first use of efficient evolutionary algorithms within a high-throughput multicellular computing approach to find therapeutic design optima that maximise tumour regression. [ABSTRACT FROM AUTHOR]

The optimal design of dispersion nuclear fuel necessitates precise correlations between the particle distribution and mechanical properties, which can be established by combining the numerical method, data-driven technique, and machine learning model. In this study, we developed an automated high-throughput workflow to rapidly generate massive number of dispersion nuclear fuel meat models with different particle distributions, and further built the machine learning database for predicting the crucial mechanical properties. Effects of the particle-agglomeration behavior on mechanical properties of the dispersion fuel meat were analyzed in length. The automated workflow includes the entire parametric modeling, parallel computation and post-processing of simulated results, through which we can calculate the equivalent elastic modulus and the maximum Mises stress of the dispersed microstructure with randomly distributed fuel particles. The Fourier distribution function is utilized to characterize the random particle distribution configuration. The analysis suggests that the particle distribution configuration, the volume fraction and the number of agglomerate particles are the crucial factors determining the performance of dispersion nuclear fuel. Furthermore, through utilizing the database constructed by high-throughput computing, the Gaussian process regression algorithm was successfully applied to accurately forecast the mechanical properties in the dispersion fuel meat. This work lays a foundation for optimizing the design of high-performance dispersion nuclear fuel, quickly estimating mechanical properties of composite structures containing randomly dispersed particles, and further extending to analogous systems. [ABSTRACT FROM AUTHOR]

Vicente Galiano,1 Pablo Garcia-Valtanen,2 Vicente Micol,3,4 José Antonio Encinar3 1Physics and Computer Architecture Department, Miguel Hernández University (UMH), Elche, Spain; 2Experimental Therapeutics Laboratory, Hanson and Sansom Institute for Health Research, School of Pharmacy and Medical Science, University of South Australia, Adelaide, Australia; 3Molecular and Cell Biology Institute, Miguel Hernández University (UMH), Elche, Spain; 4CIBER: CB12/03/30038, Physiopathology of the Obesity and Nutrition, CIBERobn, Instituto de Salud Carlos III, Palma de Mallorca, Spain Abstract: The dengue virus (DENV) nonstructural protein 5 (NS5) contains both an N-terminal methyltransferase domain and a C-terminal RNA-dependent RNA polymerase domain. Polymerase activity is responsible for viral RNA synthesis by a de novo initiation mechanism and represents an attractive target for antiviral therapy. The incidence of DENV has grown rapidly and it is now estimated that half of the human population is at risk of becoming infected with this virus. Despite this, there are no effective drugs to treat DENV infections. The present in silico study aimed at finding new inhibitors of the NS5 RNA-dependent RNA polymerase of the four serotypes of DENV. We used a chemical library comprising 372,792 nonnucleotide compounds (around 325,319 natural compounds) to perform molecular docking experiments against a binding site of the RNA template tunnel of the virus polymerase. Compounds with high negative free energy variation (ΔG