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59 results for “Material optimization”
Materials Science Optimization Benchmark Dataset for High-dimensional, Multi-objective, Multi-fidelity Optimization of CrabNet Hyperparameters
Benchmarks are an essential driver of progress in scientific disciplines. Ideal benchmarks mimic real-world tasks as closely as possible, where insufficient difficulty or applicability can stunt growth in the field. Benchmarks should also have sufficiently low computational overhead to promote accessibility and repeatability. The goal is then to win a "Turing test" of sorts by creating a surrogate model that is indistinguishable from the ground truth observation (at least within the dataset bounds that were explored), necessitating a large amount of data. In materials science and chemistry, industry-relevant optimization tasks are often hierarchical, noisy, multi-fidelity, multi-objective, high-dimensional, and non-linearly correlated while exhibiting mixed numerical and categorical variables subject to linear and non-linear constraints. To complicate matters, unexpected, failed simulation or experimental regions may be present in the search space. In this study, 173219 quasi-random hyperparameter combinations were generated across 23 hyperparameters and used to train CrabNet on the Matbench experimental band gap dataset. The results were logged to a free-tier shared MongoDB Atlas dataset. This study resulted in a regression dataset mapping hyperparameter combinations (including repeats) to MAE, RMSE, computational runtime, and model size for CrabNet model trained on the Matbench experimental band gap benchmark task1. This dataset is used to create a surrogate model as close as possible to running the actual simulations by incorporating heteroskedastic noise. Failure cases for bad hyperparameter combinations were excluded via careful construction of the hyperparameter search space, and so were not considered as was done in prior work. For the regression dataset, percentile ranks were computed within each of the groups of identical parameter sets to enable capturing heteroskedastic noise. This contrasts with a more traditional approach that imposes a-priori assumptions such as Gaussian noise, e.g., by providing a mean and standard deviation. A similar approach can be applied to other benchmark datasets to bridge the gap between optimization benchmarks with low computational overhead and realistically complex, real-world optimization scenarios.
Performance analysis & optimization of inverted inorganic CsGeI3 perovskite cells with carbon/copper charge transport materials using SCAPS‑1D
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Improving reconstructions in nanotomography for homogeneous materials via mathematical optimization
<p>This is the raw data for the manuscript:</p><p>Improving reconstructions in nanotomography for homogeneous materials via mathematical optimization.</p><p>A readme file containing all descriptions can be found in the main folder. All data are sorted in a separate subfolder each according to the three different types of datasets used in the main manuscript. Further, the Python scripts used for tomographic reconstruction are to be found in the zip file.</p><p>Abstract:</p><p>Compressed sensing is an image reconstruction technique to achieve high-quality results from limited amount of data. In order to achieve this, it utilizes prior knowledge about the samples that shall be reconstructed. Focusing on image reconstruction in nanotomography, this work proposes enhancements by including additional problem-specific knowledge. In more detail, we propose further classes of algebraic inequalities that are added to the compressed sensing model. The first consists in a valid<br>upper bound on the pixel brightness. It only exploits general information about the projections and is thus applicable to a broad range of reconstruction problems. The second class is applicable whenever the sample material is of roughly homogeneous composition. The model favors a constant density and penalizes deviations from it. The resulting mathematical optimization models are algorithmically tractable and can be solved to global optimality by state-of-the-art available implementations of interior point methods. In order to evaluate the novel models, obtained results are compared to existing image reconstruction methods, tested on simulated and experimental data sets. The experimental data comprise one 360° electron tomography tilt series of a macroporous zeolite particle and one absorption contrast nano X-ray computed tomography (nano-CT) data set of a copper microlattice structure. The enriched models are optimized quickly and show improved reconstruction quality, outperforming the existing models. Promisingly, our approach yields superior reconstruction results, particularly when information about the samples is available for a small number of tilt angles only.</p>
Multi-objective optimization of a renewable fuel supply chain regarding cost, land use, and water use - Supplementary Materials
<p><strong>This repository contains supporting data for: "Multi-objective optimization of a renewable fuel supply chain regarding cost, land use, and water use"</strong></p> <ol> <li> <p><strong>Input Data</strong>: This file includes the model input data.</p> </li> <li> <p><strong>Payoff Matrix Solution</strong>: This file contains all decision variables of payoff matrix solutions (Cost optimal, Land-use optimal, and Water-use optimal solution).</p> </li> <li><strong>Compromise solution</strong>: This file contains all decision variables of the compromise solution.</li> <li> <p><strong>Pareto Curve</strong>: This file presents the Pareto curve solution. </p> </li> <li><strong>Pareto Curve Solution</strong>: This file contains all decision variables in each of the Pareto curve solutions. </li> </ol>
Supplementary material from: Optimizing the Conversion of Bio-Oil from Haematococcus pluvialis to Fatty Acid Methyl Esters
<p>Data obtained in the thermal characterization of bio-oil and biodiesel derived from Haematococcus pluvialis microalgae. This document contains the data used to generate the plots shown in Figure 3 of the paper.</p> <p>Content:</p> <ul> <li>Bio-oil FTIR results</li> <li>Biodiesel FTIR results</li> <li>Bio-oil TGA results</li> <li>Biodiesel TGA results</li> <li>Bio-oil DSC (pour point) results</li> <li>Biodiesel DSC (pour point) results</li> <li>Biodiesel DSC (heat of combustion) results</li> </ul>
Overcoming contrast reversals in focused probe ptychography of thick materials: an optimal pipeline for efficiently determining local atomic structure in materials science
<p>Files concerning the publication "Overcoming contrast reversals in focused probe ptychography of thick materials: an optimal pipeline for efficiently determining local atomic structure in materials science"(arxiv:2205.13308 )</p>
Experimental data linked to publication "Process optimization and study of the co-sintering behaviour of Cu-Ni multi-material 3D structures fabricated by spark plasma sintering (SPS)"
<p>Those are all the experimental data used to produce the plots in the article</p>
Dataset related to "High-frequency optimally windowed chirp rheometry for rapidly evolving viscoelastic materials: Application to a crosslinking thermoset"
<p>Knowledge of the evolution in the mechanical properties of a curing polymer matrix is of great importance in composite parts or structure<br>fabrication. Conventional rheometry, based on small amplitude oscillatory shear, is limited by long interrogation times. In rapidly evolving<br>materials, time sweeps can provide a meaningful measurement albeit at a single frequency. To overcome this constraint, we utilize a combined<br>frequency- and amplitude-modulated chirped strain waveform in conjunction with a homemade sliding plate piezo-operated rheometer (PZR)<br>and a dual-head commercial rotational rheometer (Anton Paar MCR 702) to probe the linear viscoelasticity of these time-evolving materials.<br>The direct controllability of the PZR, resulting from the absence of any kind of firmware and the microsecond actuator-sensor response<br>renders this device ideal for exploring the advantages of this technique. The high frequency capability allows us to extend the upper limits of<br>the accessible linear viscoelastic spectrum and, most importantly, to shorten the length of the interrogating strain signal (OWCh-PZR) to subsecond<br>scales, while retaining a high time-bandwidth product. This short duration ensures that the mutation number (NMu) is kept sufficiently<br>low, even in fast-curing resins. The method is validated via calibration tests in both instruments, and the corresponding limitations are discussed.<br>As a proof of concept, the technique is applied to a curing vinylester resin. The linear viscoelastic (LVE) spectrum is assessed every<br>20 s to monitor the rapid evolution in the time and frequency dependence of the complex modulus. Comparison of the chirp implementation,<br>based on parameters such as duration of the experiment, sampling frequency, and frequency range, in a commercial rotational rheometer with<br>the PZR provides further information on the applicability of this technique and its limitations. Finally, FTIR spectroscopy is utilized to gain<br>insights into the evolution of the chemical network, and the gap dependence of the evolving material properties in these heterogeneous<br>systems is also investigated</p>
Applicability of new sustainable and efficient alginate-based composites for critical raw materials recovery: General composites fabrication optimization and adsorption performance evaluation
<p>This dataset contains the raw data for the publication "Applicability of new sustainable and efficient alginate-based composites for critical raw materials recovery: General composites fabrication optimization and adsorption performance evaluation" by Fila et al, published in Chemical Engineering Journal. The upload includes raw data of physicochemical characterizations of calcium alginate and its composites, i.e. alginate-biochar and alginate-clinoptilolite, including BET, SEM, TG, XPS and XRD analyses. </p>
Planning as Optimization: Online Learning of Situations and Optimal Configurations - SASO 2019 - Accompanying material
<p>These files are accompanying material for our submission "" to SASO 19:</p> <p>Many approaches apply optimization techniques in SASs, mostly within the planning procedure, to generate new system configurations or adaptation plans. We performed an analysis of these techniques based on approaches published during the last ten years in conferences and journals related to self-adaptive systems (SASs), namely ACM Transactions on Autonomous and Adaptive System (TAAS), the International Conference on Autonomic Computing and Communications (ICAC), the International Conferences on Self-Adaptive and Self-Organizing Systems (SASO), the International Symposium on Software Engineering for Adaptive and Self-Managing Systems (SEAMS), and the Symposium on the Foundations of Software Engineering (FSE). We identified the use of 29 different techniques in 51 publications. This list shows that a large set of techniques from different classes such as probabilistic, combinatorial, evolutionary, stochastic, mathematical, and meta-heuristic optimization are applied in SASs.</p> <p> </p> <p>We provide two files:</p> <p>- List of References (SASO - References - Planning_as_Optimization.pdf)</p> <p>- Dataset (SASO - Dataset - Planning_as_Optimization.xlsx)</p>
Data for: Nest material preferences in wild hazel dormice Muscardinus avellanarius: Testing predictions from optimal foraging theory
<p class="MsoNormal">Obtaining nesting material presents an optimal foraging problem, collection of materials incurs a cost in terms of risk of predation and energy spent, and individuals must balance these costs with the benefits of using that material in the nest. The hazel dormouse, <em>Muscardinus avellanarius</em> is an endangered British mammal in which both sexes build nests. However, whether material used in their construction follows the predictions of optimal foraging theory is unknown. Here, we analyse the use of nesting materials in forty two breeding nests from six locations in Southwest England. Nests were characterised in terms of which plants were used, the relative amount of each plant, and how far away the nearest source was. We find that dormice exhibit a preference for plants closer to the nest, but that the distance they are prepared to travel depends on the plant species. Dormice travelled further to collect honeysuckle <em>Lonicera periclymenum</em>, oak <em>Quercus robur</em>, and beech <em>Fagus sylvatica</em> than any other plants. Distance did not affect the relative amount used, although the proportion of honeysuckle in nests was highest, and more effort was expended collecting honeysuckle, beech, bramble <em>Rubus fruticosus</em> and oak compared to other plants. Our results suggest that not all aspects of optimal foraging theory apply to nest material collection. However, optimal foraging theory is a useful model to examine nest material collection, providing testable predictions. As found previously honeysuckle is important as a nesting material, and should be taken account when assessing suitability of sites for dormice.</p>
Supplementary material and dataset for article "Structure-Function Relationship of Iron Oxide Nanoflowers: Optimal Sizes for Magnetic Hyperthermia Depending on Alternating Magnetic Field Conditions"
<p>The supporting information file contains additional measurements compared to the main manuscript of the related article: TEM size histograms, SAED and XRD patterns, absorption and fluorescence spectra, DC magnetization curves, AC hysteresis loops, HR-TEM, their FTT patterns and inverse FFT images after applying a mask in the reciprocal space, and various other plots of this multi-parametric study on the structure-properties relations of magnetic iron oxide nanoflowers. Whenever needed for comparison to other experimental results or theoretical fitting, the raw data of all DC magnetization curves, AC hysteresis loops, ZFC-FC magnetization curves vs. temperature (and their derivatives on temperature) are made freely available in this dataset.</p>
Data for: Nest material preferences in wild hazel dormice Muscardinus avellanarius: Testing predictions from optimal foraging theory
Open the record for dataset details and reuse information.
A multiscale optimization framework for bone remodeling: Integrating material and structural adaptations across hierarchical scales
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Supplementary material to `Accelerating Lagrangian transport simulations on graphics processing units: performance optimizations of MPTRAC v2.6'
<p>This repository contains the supplementary material to the following paper: Hoffmann, L., Haghighi Mood, K., Herten, A., Hrywniak, M., Kraus, J., Clemens, J., and Liu, M., Accelerating Lagrangian transport simulations on graphics processing units: performance optimizations of MPTRAC v2.6, Geoscientific Model Development, submitted, 2023.</p>
Supplementary material for publication "Optimizing combined tours: The truck-and-cargo-bike case"
<p>Supplementary material for publication "Optimizing combined tours: The truck-and-cargo-bike case" in OR Spectrum.</p> <p>Includes datasets I_1, I_2_W and I_2_M for different values of delta and n and results for the MIP formulations and heuristics.</p> <p> </p> <p> </p> <p>https://doi.org/10.1007/s00291-024-00754-2</p>
Screener and Enumerator with Force-Field Optimization (SEFFO): algorithm for searching adsorption sites and configurations on 2D materials
<p>See ref:</p> <p> </p> <p>(submission stage)</p>
Supplementary Material for "High spatial resolution photogrammetry and LiDAR in the Cádiz Bay (SW, Spain): optimizing the application of UAV-techniques to salt marshes" article.
<p>Data presented in the study "High spatial resolution photogrammetry and LiDAR in the Cádiz Bay (SW, Spain): optimizing the application of UAV-techniques to salt marshes" </p>
Dataset: Protocol for Designing, Optimizing and Analyzing Secondary Critical Material Supply Chains using RELOG
<p>Source code and data for "Protocol for Designing, Optimizing and Analyzing Secondary Critical Material Supply Chains using RELOG."</p>
Supplementary Material for the Paper "Optimizing Workday Planning Based on Time and Productivity Constraints"
<p>This repository contains the (anonymized) supplementary material for our paper. This includes the survey questions we asked, as well as a video and screenshots of DayO.</p> <p><strong>Abstract</strong>: Planning one’s workday was shown to be positively related to increased control of time, work and life satisfaction, and<br> performance. However, planning for an efficient workday is challenging, due to the high complexity of tasks that knowledge<br> workers have to perform and the many constraints that influence their plan. Few planning tools actively support users with the task scheduling process, but they do not take into account these time and productivity constraints. Hence, we have devised an automated task scheduling approach that recommends an optimized workday plan, by considering constraints such as task characteristics (e.g., difficulty, deadline, importance), scheduled appointments, the current stress level and sleep quality from the previous night. A preliminary analysis of the approach, using DayO as a technology probe in situ, showed that participants found the idea<br> very promising, but a real-world application would additionally require further support to reduce the cognitive burden of planning and would let users to adapt their schedules interactively, when constraints change throughout a workday.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.