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Search results for machine learning materials informatics
machine-learning
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materials-informatics
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24 search results found
Bestpractices
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142
Things that you should (and should not) do in your Materials Informatics research.
Chemml
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142
ChemML is a machine learning and informatics program suite for the chemical and materials sciences.
Pymatviz
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98
A toolkit for visualizations in materials informatics.
Catlearn
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82
A machine learning environment for atomic-scale modeling in surface science and catalysis.
Smact
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69
Python package to aid materials design and informatics
Crabnet
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65
Predict materials properties using only the composition information!
Self Driving Lab Demo
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54
Software and instructions for setting up and running a self-driving lab (autonomous experimentation) demo using dimmable RGB LEDs, an 8-channel spectrophotometer, a microcontroller, and an adaptive design algorithm, as well as extensions to liquid- and solid-based color matching demos.
Moldqn Pytorch
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40
A PyTorch Implementation of "Optimization of Molecules via Deep Reinforcement Learning".
Xtal2png
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28
Encode/decode a crystal structure to/from a grayscale PNG image for direct use with image-based machine learning models such as Palette.
Mat_discover
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27
A materials discovery algorithm geared towards exploring high-performance candidates in new chemical spaces.
Mlformaterials
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24
Online resource for a practical course in machine learning for materials research at Imperial College London (MATE70026)
Slices
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22
SLICES: An Invertible, Invariant, and String-based Crystal Representation (Text2Crystal)
Polygnn
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22
polyGNN is a Python library to automate ML model training for polymer informatics.
Pdyna
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21
Python package to analyse the structural dynamics of perovskites
Molecular Vae
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19
Pytorch implementation of the paper "Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules"
Pumml
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11
Positive and Unlabeled Materials Machine Learning (pumml) is a code that uses semi-supervised machine learning to classify materials from only positive and unlabeled examples.
Mpds Ml Labs
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10
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).
S4
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9
Solid-state synthesis science analyzer. Thermo, features, ML, and more.
Interp
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7
Interpolate grain boundary properties in a 5 degree-of-freedom sense via a novel distance metric.
Smact_workflows
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7
Computational experiments using SMACT for materials design
Esse
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6
JSON schemas and examples representing structural data, characteristic properties, modeling workflows and related data about materials standardizing the diverse landscape of information
Discover Supercon Nomad Smact
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6
Composition-based predictions for chemically novel, high-temperature superconductors.
Mdn Gan
⭐
6
A General Framework Combining Generative Adversarial Networks and Mixture Density Networks for Inverse Modeling in Microstructural Materials Design
Fuller
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6
Integrated computational framework for reconstruction and parametrization of electronic band sturcture from photoemission spectroscopy data
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