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82 results for “properties prediction”
Solution #4 for Predicting Molecular Properties Kaggle Competition
<p>Code and additional data for solution #4 in <a href="https://www.kaggle.com/c/champs-scalar-coupling/overview">Predicting Molecular Properties</a> competition, described in <a href="https://www.kaggle.com/c/champs-scalar-coupling/discussion/106534#latest-613848">#4 Solution [Hyperspatial Engineers]</a>.</p>
Data set related to the manuscript "On the development of an original mesoscopic model to predict the capacitive properties of carbon-carbon supercapacitors"
<p>Graphical files in the agr format for all the figures in the main text of the manuscript entitled "On the development of an original mesoscopic model to predict the capacitive properties of carbon-carbon supercapacitors" (<a href="https://doi.org/10.1016/j.electacta.2019.135022">10.1016/j.electacta.2019.135022</a>).</p>
Out-of-Distribution Evaluation of Active Learning Pipelines for Molecular Property Prediction Dataset
<p>Data for the paper "Out-of-Distribution Evaluation of Active Learning Pipelines for Molecular Property Prediction"</p>
Dataset for "Generalizing property prediction of ionic liquids from limited labeled data: a one-stop framework empowered by transfer learning"
<p>Dataset for "Generalizing property prediction of ionic liquids from limited labeled data: a one-stop framework empowered by transfer learning", codes can be found <a href="https://github.com/GuzhongChen/ILTransR">here</a>.</p>
Data sets and machine learning models for: Predicting critical properties and acentric factor of fluids using multi-task machine learning
<p>The experimental data sets, data splits, additional features, QM calculations, model predictions, and final machine learning models for the manuscript "Predicting Critical Properties and Acentric Factor of Fluids Using Multi-Task Machine Learning". <strong>Citation should refer directly to the manuscript:</strong></p> <ul> <li> <p>Biswas, S.; Chung, Y.; Ramirez, J.; Wu, H.; Green, W. H. Predicting Critical Properties and Acentric Factors of Fluids Using Multitask Machine Learning. <em>Journal of Chemical Information and Modeling.</em> <strong>2023</strong> <em>63</em> (15), 4574-4588. DOI: <a href="https://doi.org/10.1021/acs.jcim.3c00546">10.1021/acs.jcim.3c00546</a></p> </li> </ul> <p>To use the machine learning models, please refer to the sample files and instructions on <a href="https://github.com/yunsiechung/chemprop/tree/crit_prop">https://github.com/yunsiechung/chemprop/tree/crit_prop</a>. </p> <p>Detailed information can be found in README.md file.</p> <p> </p> <p><strong>Details on the properties considered</strong></p> <p>The data set includes the following 8 properties:</p> <ul> <li>Tc: critical temperature, in K</li> <li>Pc: critical pressure, in bar</li> <li>rhoc: critical density, in mol/L</li> <li>omega: acentric factor, unitless</li> <li>Tb: boiling point, in K</li> <li>Tm: melting point, in K</li> <li>dHvap: enthalpy of vaporization at boiling point, in kJ/mol</li> <li>dHfus: enthalpy of fusion at melting point, in kJ/mol</li> </ul> <p><strong>Details on the files</strong></p> <p>1. Data sets under CritProp_v1.1.0:</p> <ul> <li>all_data: includes the data sets used in this work. All data points are listed for each chemical compound as well as its corresponding data source. The details of the data sources can be found in the README.md file. The distribution of the data set is included in each folder. <ul> <li>estimated_data_for_pretraining: contains the estimated data from Yaws' handbook that are used to pre-train our machine learning (ML) model.</li> <li>experimental_data: contains the experimental data (references 1 - 15) used to fine-tune our final ML model.</li> </ul> </li> <li>additional_features: includes the additional features tested for the ML model. The Abraham features are generated for all data (references 1 - 15) while the acsf, qm, and rdkit features are only generated for the data from references 1 - 9. <ul> <li>abraham: Abraham solute parameters (E, S, A, B, L). Molecular features.</li> <li>acsf: ACSF (atom-centered symmetry functions). Atomic features that are coverted from the 3D coordinates of the compound</li> <li>qm_atom: QM (quantum chemical) atomic feature. </li> <li>qm_mol: QM molecular feature.</li> <li>rdkit: Selected RDKit 2D molecular features.</li> </ul> </li> <li>data_splits_and_model_predictions: contains the training and test sets used to evaluate the model. It also contains the predicted values from our final ML model for each test set. <ul> <li>random and scaffold splits: training and test sets that include the data from references 1 - 9.</li> <li>external test set: a test set that includes the data from only references 10 - 15.</li> </ul> </li> </ul> <p>2. Machine learning (ML) model files:</p> <ul> <li>CritProp_ML_model_files_with_abraham_feat.zip: contains the Chemprop ML model files that are trained using Abraham features as additional molecular features. This gives the best results.</li> <li>CritProp_ML_model_files_without_additional_feat.zip: contains the Chemprop ML model files that are trained without any additional features. This gives the second best results.</li> </ul> <p>To use these ML models, please refer to the sample files and instructions on <a href="https://github.com/yunsiechung/chemprop/tree/crit_prop">https://github.com/yunsiechung/chemprop/tree/crit_prop</a></p> <p>3. QM (quantum chemical) calculations:</p> <ul> <li>QM_calculations.zip: contains the results of the QM calculations that are performed to compute QM features.</li> </ul> <p> </p> <p> </p>
Sequence/simulation data for Direct Prediction of Intrinsically Disordered Protein Conformational Properties From Sequence
<p>This is a DOI-linked deposition of sequence/biophysical properties pairs used in the associated paper by Lotthammer et al:</p><p>Lotthammer, J. M.<strong>*</strong>, Ginell, G. M.<strong>*</strong>, Griffith, D.<strong>*</strong>, Emenecker, R. J. & Holehouse, A. S. <br>Direct Prediction of Intrinsically Disordered Protein Conformational Properties From Sequence.<br><i><strong>Nature Methods</strong></i> (<i>in press</i>), (2023).</p><p> </p>
To enhance CO2 saturation prediction from seismic data by joint use of attenuation and elastic properties: A machine learning approach
<p>Wang and Zhao submitted for GJI</p>
Finite element dataset and Artificial Neural Networks algorithms to predict the mechanical properties of innovative CLT
<p>This folder includes the data collected from the finite element simulations of the innovative CLT to compute its mechanical properties, the error of the closed-form solutions predicting the bending stiffness in the minor direction D22, the variation of the distance between the Reissner Mindlin and Bending Gradient theory in terms of spacing between lateral lamellas, the hyperparameters tuning of several Artificial Neural Networks algorithms with or without prior knowledge, the ML evaluations, the saved artificial neural network algorithms to predict each mechanical property of innovative CLT, and the ML application to use it.</p>
Datasets for understanding the importance of conformation in property prediction models
<p>Descriptor and conformer data sets for molecular property and reaction selectivity prediction tasks. The PQC data set was created based on a part of the PubChemQC PM6 dataset (J. Chem. Inf. Model. 2020, 60, 12, 5891–5899), which contains two- and three-dimensional descriptors and conformers. The APTC data sets are based on the data sets for asymmetric phase transfer catalysts with enantio-selectivity (<a href="https://github.com/Laboratoire-de-Chemoinformatique/3D-MIL-QSSR/tree/main/datasets" target="_blank" rel="noopener">https://github.com/Laboratoire-de-Chemoinformatique/3D-MIL-QSSR/tree/main/datasets</a>). The melting point data set was created from the Jean-Claude Bradley Double Plus Good (Highly Curated and Validated) Melting Points Dataset (<a href="https://doi.org/10.6084/m9.figshare.1031638.v1">https://doi.org/10.6084/m9.figshare.1031638.v1</a>).</p> <p>They contained descriptors and conformers to train and validate machine learning models.</p> <p>Detailed explanations on how to use these datasets are found in the Github repository: <a href="https://github.com/YuHamakawa/Conformation-Importance-ML-Models">https://github.com/YuHamakawa/Conformation-Importance-ML-Models</a>. </p> <p> </p> <p> </p>
Morphological profiling data resource enables prediction of chemical compound properties
<p><span><span>Morphological profiling with the Cell Painting assay has </span><span>emerged</span><span> as a promising method in drug discovery research. The assay captures morphological changes across various cellular compartments enabling the rapid prediction of compound </span></span><span><span><span>bioactivity</span></span></span><span><span>. We present a comprehensive morphological profiling resource using the carefully curated and well-annotated EU-OPENSCREEN Bioactive Compounds. </span><span>The data was generated across four imaging sites with high-throughput confocal microscopes using the </span><span>Hep</span><span> G2 as well as the U2 OS cell line.</span><span> We employed an extensive assay optimization process to achieve high data quality across the different sites. An analysis of the extracted profiles </span><span>validates</span><span> the robustness of the generated data. <span>We used this resource to compare the morphological features of the different cell lines. By correlating the profiles with overall activity, cellular toxicity, several specific mechanisms of action (MOAs), and protein targets, we </span><span>demonstrate</span><span> the dataset's potential for </span><span>facilitating</span><span> more extensive exploration of mechanisms of action.</span> </span></span></p>
Analytical prediction of scattering properties of spheroidal dust particles with machine learning
<p>This respository includes the data used in the paper "Analytical prediction of scattering properties of spheroidal dust particles with machine learning".</p> <ol> <li>"alldata.zip" represents the extinction and absorption coefficients, and phase matrix elements of spheroids dust particles for both training and test data. These data comes from the Oleg Dubovik's group: <a href="https://www.grasp-open.com/products/spheroid-package-release">https://www.grasp-open.com/products/spheroid-package-release</a>/.</li> <li>"<a href="/api/files/7c2cdcdd-dc47-4a19-adc8-59c944e76e54/tmat_Jacall_norm_intg.pickle?versionId=59294030-26a8-4683-9b26-8e8584c2b44b">tmat_Jacall_norm_intg.pickle</a>" contains the Jacobians simulated from linearized T-matrix model used for training in the paper and "<a href="/api/files/7c2cdcdd-dc47-4a19-adc8-59c944e76e54/tmat_fine_n_intg.pickle?versionId=3d20954d-4318-40bb-a900-a93627915c2e">tmat_fine_n_intg.pickle</a>" involves Jacobians used for testing.</li> </ol>
Dataset 2 "Aerosol optical properties within the atmospheric boundary layer predicted from ground-based observations compared to Raman lidar retrievals during RITA-2021"
<p>This dataset provides additional profiles for the manuscript titled "Aerosol optical properties within the atmospheric boundary layer predicted from ground-based observations compared to Raman lidar retrievals during RITA-2021". It is available for those who are interested.</p>
Chemperium database for: Geometric Deep Learning for Molecular Property Predictions with Chemical Accuracy Across Chemical Space
<p>The dataset and trained models for the submitted manuscript "Geometric Deep Learning for Molecular Property Prediction with Chemical Accuracy Across Chemical Space"</p> <p>The trained models can be used in combination with the predict module in github.com/mrodobbe/chemperium. More information in README.md.</p> <p><em>When using these datasets, refer directly to the manuscript: https://doi.org/10.1186/s13321-024-00895-0 </em></p>
An adaptive and interpretable modeling architecture assisted rapid and reliable consensus prediction for hazardous properties of chemicals
<p>*the computational results of interpretable cases are available in the supporting information for interpretable case.xlsx ;</p> <p>*the training dataset is utilized for model training while the validation dataset is utilized for evaluating. </p>
Modelling and prediction of the thermophysical properties of aqueous mixtures of choline geranate and geranic acid (CAGE) using SAFT-g Mie
<p>Calculated and experimental data for all the figures in the publication </p>
Descriptor and Graph-based Molecular Representations in Prediction of Copolymer Properties using Machine Learning
<p>This dataset accompanies a study that investigates the use of machine learning (ML) approaches for predicting seven different physical properties of 140 binary copolymers. Two computational methods were employed: a random forest (RF) model based on molecular descriptors and a Graph Neural Network (GNN) using 2D polymer graphs. These methods were applied in both single- and multi-task settings to explore the strengths of each approach in capturing various polymer properties.</p> <p>The dataset includes two files:</p> <ol> <li> <p><strong>Dataset.xlsx</strong>: Contains the following information for each of the 140 copolymers:</p> <ul> <li>Polymer names</li> <li>SMILES notation of the monomers</li> <li>Fraction of monomers in each copolymer</li> <li>Simulated values (calculated using molecular dynamics simulation) and experimental values of various physical properties, including density, specific heat capacity at constant pressure ad volume ,radius of gyration, linear expansion coefficient, volume expansion coefficient, and bulk modulus .</li> </ul> </li> <li> <p><strong>Descriptors.xlsx</strong>: Provides the molecular descriptors calculated using PaDEL-Descriptor software, which were used as input for the RF model to predict polymer properties.</p> </li> </ol> <p>This work provides insight into the comparative strengths of descriptor- and graph-based representations in machine learning models for predicting material properties. It also highlights the importance of selecting appropriate molecular representations based on the nature of the properties being predicted.</p>
Dataset for the paper 'Predicting mechanical properties of polycrystalline nanopillars by interpretable machine learning'
<div> <div>This dataset contains the data produced for the above paper. The dataset consists of:</div> <div> </div> <div>- input nanopillars before deformation (molecular_dynamics/nanopillars)</div> <div>- stress-strain curves acquired by deforming the nanopillars (molecular_dynamics/stress_strain_curves)</div> <div>- weights of the CNNs trained to predict mechanical properties of the nanopillars (machine_learning/train_CNN)</div> <div>- Grad-CAM fields of the predictions (machine_learning/train_CNN)</div> <br> <div>Codes used for creating and analyzing the dataset are available at https://github.com/tekoivisto/nanopillar-ML</div> </div>
ActTemp+sMSA dataset of "Biasing AlphaFold2 to predict GPCRs and Kinases with user-defined functional or structural properties"
<p>PDB models generated with the protocol described in "Biasing AlphaFold2 to predict GPCRs and Kinases with user-defined functional or structural properties"</p>
Data from: Thermal failure of diamond tools indicated by diamond degradation: Damage evaluation and property prediction on small image datasets
<p>High temperature induced diamond degradation often leads to the failure of diamond tools. In this work, diamond samples holding different degrees of thermal damage were prepared by heating and sintering. The influence of diamond particle size and processing temperature was investigated through mechanical testing and micromorphology observation, meanwhile, a dataset containing 2870 SEM images showing diamonds with different degrees of degradation was constructed. By modification of VGG16 network, classification models and regression models were developed for thermal damage evaluation and sample property prediction. Training strategies including transfer learning and data augmentation were implemented and verified essential on the small dataset, where drop-out showed no positive effects. Two classification models (3-class and 65-class) were constructed and trained for damage evaluation. Visualized damage feature maps exported from Grad-CAM revealed the influential mechanism of thermal damage on diamonds, which proved the effectiveness of the classification models as well. Under the optimized training strategies, regression models were built for sample property prediction. The models towards toughness index, bending strength loss, relative density and Rockwell hardness were examined. Comparing the output results with real property values in test sets, the first two models matched well, and the latter two showed the opposite. It verified the validity of the regression models for property prediction as they were all established based on diamond damage image datasets. The loss in bending strength loss prediction model was smaller than that of toughness index, indicating bending strength easier to be shorten than impact toughness for diamond/metal composites suffering thermal impacts.</p>
Source data belonged to "Establishing structure-property linkages for wicking time predictions in porous polymeric membranes using a data-driven approach"
<p>This record contains all the necessary data to obtain the results of the study "Establishing structure-property linkages for wicking time predictions in porous polymeric membranes using a data-driven approach"</p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.