Mpds Ml Labs
This is the proof of concept, how a relatively unsophisticated statistical model trained on the large MPDS dataset predicts physical properties from the only crystalline structure (POSCAR or CIF).
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Automatminer | 89 | 2 years ago | 12 | July 28, 2020 | 47 | other | Python | |||
| An automatic engine for predicting materials properties. | ||||||||||
| Modnet | 62 | 7 months ago | 22 | July 29, 2023 | 26 | mit | Jupyter Notebook | |||
| MODNet: a framework for machine learning materials properties | ||||||||||
| Molecules_dataset_collection | 26 | 6 years ago | mit | |||||||
| Collection of data sets of molecules for a validation of properties inference | ||||||||||
| Gazebo_domain_randomization | 25 | 5 years ago | bsd-3-clause | C++ | ||||||
| Domain randomization for gazebo simulator | ||||||||||
| Mpds Ml Labs | 10 | 2 years ago | 4 | lgpl-2.1 | HTML | |||||
| This is the proof of concept, how a relatively unsophisticated statistical model trained on the large MPDS dataset predicts physical properties from the only crystalline structure (POSCAR or CIF). | ||||||||||
| Nexosisclient Py | 7 | 1 | 6 years ago | 19 | April 12, 2018 | other | Python | |||
| Python client for the Nexosis API | ||||||||||
| Pyxtal_ml | 7 | 5 years ago | 7 | mit | Python | |||||
| a Python3 library for ML modeling materials properties | ||||||||||
| Graphviz Treevisualize Weka Package | 6 | 2 years ago | 4 | April 13, 2022 | gpl-3.0 | Java | ||||
| TreeVisualize plugin for the Weka Explorer using GraphViz (http://www.graphviz.org/) to generate the tree. | ||||||||||
| Winecoreml | 6 | 7 years ago | mit | Swift | ||||||
| iOS app that predicts the origin of wines using a Core ML model | ||||||||||
| Sca2017 | 5 | 4 years ago | other | C++ | ||||||
| This project implements the algorithms described in the paper "Augmenting Sampling Based Controllers with Machine Learning" | ||||||||||
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