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Dataset results
53 results for “machine learning potential”
Source Data for the manuscript: General-purpose machine-learned potential for 16 elemental metals and their alloys
<p>***Source Data***<br>This folder contains multiple .txt files that provide the source data for the figures and tables presented in the paper: "General-purpose machine-learned potential for 16 elemental metals and their alloys."</p> <p>The source data are organized in the following folders and files:</p> <p>1) Fig2<br>2) Fig3<br>3) Fig4<br>4) Fig5<br>5) Fig6<br>6) FigS1<br>7) FigS2<br>8) FigS3<br>9) FigS4-6-pure<br>10) FigS7-9-binary<br>11) FigS10-12-ternany<br>12) FigS13-15-quaternary<br>13) FigS16-17-quinary<br>14) FigS18-20<br>15) FigS21<br>16) FigS22<br>17) FigS23<br>18) FigS26<br>19) Table1-Element-atoms-GPU-Speed.txt<br>20) Table-S1-DFT-EAM-UNEP-Elastic.txt<br>21) Table-S2-DFT-EAM-UNEP-Mono-vacanc.txt<br>22) Table-s3-Surface-100-110-111-DFT-EAM-UNEP.txt<br>23) Table-S4-Melting-EAM-UNEP-Exp.txt</p>
Data related to the publication "structure and transport properties of LiTFSI-based deep eutectic electrolytes from machine-learned interatomic potential simulations"
<p>Reference training and test datasets, trained ML potential models, and input scripts for the training (Allegro) and MD simulations (LAMMPS).</p>
Exploring the configuration space of elemental carbon with empirical and machine learned interatomic potentials
<p>This dataset contains a vertical slice of the data used to generate the results found in the publication "Exploring the configuration space of elemental carbon with empirical and machine learned interatomic potentials"<br> It contains nested sampling input files and trajectory files for each potential studied, as well as the xml files and training data for the new potential, GAP-20U+gr.</p>
Data from: Assessing the potential information content of multicomponent visual signals: a machine learning approach
Careful investigation of the form of animal signals can offer novel insights into their function. Here, we deconstruct the face patterns of a tribe of primates, the guenons (Cercopithecini), and examine the information that is potentially available in the perceptual dimensions of their multicomponent displays. Using standardized colour-calibrated images of guenon faces, we measure variation in appearance both within and between species. Overall face pattern was quantified using the computer vision 'eigenface' technique, and eyebrow and nose-spot focal traits were described using computational image segmentation and shape analysis. Discriminant function analyses established whether these perceptual dimensions could be used to reliably classify species identity, individual identity, age and sex, and, if so, identify the dimensions that carry this information. Across the 12 species studied, we found that both overall face pattern and focal trait differences could be used to categorize species and individuals reliably, whereas correct classification of age category and sex was not possible. This pattern makes sense, as guenons often form mixed-species groups in which familiar conspecifics develop complex differentiated social relationships but where the presence of heterospecifics creates hybridization risk. Our approach should be broadly applicable to the investigation of visual signal function across the animal kingdom.
Machine learning potential for serpentines
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Advancing Thermal Management with Machine Learning Potentials on Boron Nitride (BN) and Other Group 13 Nitrides
<p>Machine Learning Interatomic Potentials for Bulk and Bilayer Materials (M = B, Al, Ga, and In): Extraction of Phonons and 3rd Order Interatomic Force Constants</p>
Data from: Assessing the potential information content of multicomponent visual signals: a machine learning approach
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Research data for "Hydrogen under Pressure as a Benchmark for Machine-Learning Potentials"
<p>This dataset supports the paper: "Hydrogen under Pressure as a Benchmark for Machine-Learning Potentials". The paper is online here: https://doi.org/XXXXXXXXXXXXXXXXX.</p>
Machine Learning Potentials for Metal-Organic Frameworks using an Incremental Learning Approach: Workflow and Data
<p>This repository contains input files, workflow scripts, and output datasets and interatomic potentials for a diverse set of metal-organic frameworks, as discussed in this <a href="https://chemrxiv.org/engage/chemrxiv/article-details/6363dbf718a8ccae675d2ac8">preprint paper</a>. In addition, we provide the scripts to compute the extended Hessian and subsequently the elastic constants using automatic differentiation.</p>
The Potential Value and Impact of Diagnostic Biomarkers for MAFLD Using Machine Learning Methods
ClinicalTrials.gov study NCT06061640. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Using integrative bioinformatics approaches and machine‑learning strategies to Identify potential signatures for atrial fibrillation
GEO Series GSE282504. Homo sapiens. 30 samples. Type: Expression profiling by high throughput sequencing.
Machine Learning Potential for Electrochemical Interfaces with Hybrid Representation of Dielectric Response
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Predicting the skin sensitization potential of small molecules with machine learning models trained on biologically meaningful descriptors
<p>In recent years a number of machine learning models for the prediction of the skin sensitization potential of small organic molecules have been reported and become available. These models generally perform well within their applicability domains but, as a result of the use of molecular fingerprints and other non-intuitive descriptors, the interpretability of the existing models is clearly limited. The aim of this work is to develop a strategy to replace the non-intuitive features by predicted outcomes of bioassays. We show that such replacement is indeed possible and that as few as ten interpretable, predicted bioactivities are sufficient to reach competitive performance. On a holdout data set of 257 compounds, the best model ("Skin Doctor CP:Bio") obtained an efficiency of 0.82 and an MCC of 0.52 (at the significance level of 0.20). Skin Doctor CP:Bio is available from the authors free of charge for academic research. The modeling strategies explored in this work are easily transferable and could be adopted for the development of more interpretable machine learning models for the prediction of the bioactivity and toxicity of small organic compounds.</p> <p>The corresponding research article has been published in <em>Pharmaceuticals</em> <strong>2021</strong>, <em>14</em>(8), 790, DOI: <a href="https://doi.org/10.3390/ph14080790">https://doi.org/10.3390/ph14080790</a></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.