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QFlow 2.0: Quantum dot data for machine learning
Using a modified Thomas-Fermi approximation, we model a reference semiconductor system comprising a quasi-1D nanowire with a series of five depletion gates whose voltages determine the number of quantum dots (QDs), the charges on each of the QDs, as well as the conductance through the wire. The original dataset, QFlow lite, consists of 1 001 idealized simulated measurements with gate configurations sampling over different realizations of the same type of device. Each sample data is stored as a 100 x 100-pixel map from plunger gate voltages to (i) current through the device at infinitesimal bias, (ii) output of the charge sensor evaluated as the Coulomb potential at the sensor location - the experimentally relevant parameters that can be measured, (iii) information about the number of charges on each dot (with a default value 0 for short circuit and a barrier), and (iv) a label determining the state of the device, distinguishing between a single dot, a double dot, a short circuit, and a barrier state. The expanded dataset, QFlow 2.0, consists of 1599 idealized simulated measurements stored as roughly 250 x 250-pixel maps from plunger gate voltages to (i) output of the charge sensor, (ii) net charge on each dot, and (iii) a label determining the state of the device, distinguishing between a left, central, and right single QD, a double QD, and a barrier or short circuit (no QD) state. In addition, the QFlow 2.0 dataset includes two sets of noisy simulated measurements, one with the noise level varied around 1.5 times the optimized noise level and the other one with the noise level ranging from 0 to 7 times the optimized noise level. See the "Project description" and "Data structure" documents for additional information about these datasets.Acknowledgments: This research is sponsored in part by the Army Research Office (ARO), through Grant No. W911NF-17-1-0274. The development and maintenance of the growth facilities used for fabricating samples were supported by the Department of Energy, through Grant No. DE-FG02-03ER46028. We acknowledge the use of clean room facilities supported by The National Science Foundation (NSF) through the UW-Madison MRSEC (DMR-1720415) and electron beam lithography equipment acquired with the support of the NSF MRI program (DMR-1625348). The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the ARO or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright noted herein. Any mention of commercial products is for information only; it does not imply recommendation or endorsement by NIST.
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Designing QSARs for Parameters of High-Throughput Toxicokinetic Models Using Open-Source Descriptors
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Additional details used in the methods are found in the MS Word file “S1_Dawson et al._Supporting_Information.docx”. The MS Excel file “S2_Dawson et al. Supporting Information.xlsx” contains datasets and graphical results. The Excel file sheets are as follows: S2.1 illustrates Clint hepatic flow calculations, S2.2 - 5 include training and test data sets; S2.6-7 include figures illustrating Clint model selection criteria and assemblages of model descriptors; S2.8 includes confusion matrices for evaluation Clint model, S2.9-10 include figures illustrating fup model selection criteria and assemblages of model descriptors (with ranges); S2.11 includes tables of model assessments of the Clint test set, S2.12 includes information relevant to BER calculations for the ToxCast test set, S2.13 includes information relevant to BER calculations for Tox21 chemicals, and S2.14 provides information on different transformations for fup. This dataset is associated with the following publication: Dawson, D., B. Ingle, K. Phillips, J. Nichols, J. Wambaugh, and R. Tornero-Velez. Designing QSARs for Parameters of High-Throughput Toxicokinetic Models Using Open-Source Descriptors. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 55(9): 6505, (6517).
Rapid Experimental Estimates of Physicochemical Properties
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We have performed high-throughput experimental estimates of five physicochemical properties for a set of 200 chemicals to evaluate the consistency with previous measurements, factors impacting consistency and experimental success, and the applicability domain of the new data in relation to previously measured data and predictive models. This dataset is associated with the following publication: Nicolas, C., K. Mansouri, K. Phillips, C. Grulke, A. Richard, A. Williams, J. Rabinowitz, K. Isaacs, A. Yau, and J. Wambaugh. (ENVIRONMENTAL SCIENCE and TECHNOLOGY) Rapid Experimental Estimates of Physicochemical Properties to Inform Models and Testing. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, USA, 636: 901-909, (2018).
MCV QoE End to End Access Time Software Code
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This codeset was developed for the QoE MCV end to end access time measurement methodology.
Transparency in Modeling through Careful Application of OECD’s QSAR/QSPR Principles via a Curated Water Solubility Data Set
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Figures, Tables, and QRMF for "Charles N. Lowe, Nathaniel Charest, Christian Ramsland, Daniel T. Chang, Todd M. Martin, and Antony J. Williams Chemical Research in Toxicology 2023 36 (3), 465-478 DOI: 10.1021/acs.chemrestox.2c00379". This dataset is associated with the following publication: Lowe, C., N. Charest, C. Ramsland, D. Chang, T. Martin, and A. Williams. Transparency in Modeling through Careful Application of OECD’s QSAR/QSPR Principles via a Curated Water Solubility Data Set. CHEMICAL RESEARCH IN TOXICOLOGY. American Chemical Society, Washington, DC, USA, 36(3): 465-478, (2023).
PFECA Isomers QQQ Method Dev Data
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Raw data (peak area and height, retention time) for each mass transition for each sample. This dataset is associated with the following publication: Miller, K., and M. Strynar. Improved Tandem Mass Spectrometry Detection and Resolution of Low Molecular Weight Perfluoroalkyl Ether Carboxylic Acid Isomers. Environmental Science & Technology Letters. American Chemical Society, Washington, DC, USA, 9(9): 747-751, (2022).
MODFLOW 6 models used to evaluate the accuracy of enhanced cell connectivity for simulation of flow through dipping aquifers
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This data release contains the MODFLOW 6 models described in the related Groundwater journal article (https://doi.org/10.1111/gwat.13459). The models are generalized cross-section models of a hypothetical and idealized aquifer. The models are used to examine the effects of layered or full grid connectivity with and without the XT3D option in the Node Property Flow (NPF) package of MODFLOW 6. The data release contains the models described in the paper, the 6.5.0 binary executable for MODFLOW 6, the 6.5.0 MODFLOW source code, and the Python jupyter notebook and related Python utilities used to generate, run, and post-process the results.