CrabNet
data-resources-for-materials-science
CrabNet | data-resources-for-materials-science | |
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1 | 3 | |
96 | 302 | |
- | 6.3% | |
3.7 | 3.8 | |
almost 2 years ago | 29 days ago | |
Python | ||
MIT License | - |
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CrabNet
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Artificial intelligence can revolutionise science
I don't know. As for "literature-based discovery," this project/paper sounded like a pretty big deal when it came out a few years ago: https://github.com/materialsintelligence/mat2vec . And I see this thing came out more recently: https://github.com/anthony-wang/CrabNet .
Of course not all fields lend themselves as well to this as does materials science.
data-resources-for-materials-science
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What is the Most Comprehensive and Detailed Database For Materials?
You can check this github repository.
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Getting started with Materials Informatics
When you want to practice with datasets or you need additional data for your future studies, I’d prepared a list of materials data, I hope it may help (link is below). But, of course, there are cerrain studies in which you won’t need data at all.. Data Resources for Materials Science.
- Data Resources for Materials Science
What are some alternatives?
query-selector - LONG-TERM SERIES FORECASTING WITH QUERYSELECTOR – EFFICIENT MODEL OF SPARSEATTENTION
pymatgen - Python Materials Genomics (pymatgen) is a robust materials analysis code that defines classes for structures and molecules with support for many electronic structure codes. It powers the Materials Project.
ProteinStructurePrediction - Protein structure prediction is the task of predicting the 3-dimensional structure (shape) of a protein given its amino acid sequence and any available supporting information. In this section, we will Install and inspect sidechainnet, a dataset with tools for predicting and inspecting protein structures, complete two simplified implementations of Attention based Networks for predicting protein angles from amino acid sequences, and visualize our predictions along the way.
Quantum_Espresso_Colab - This repository includes a notebook to run the open-source materials modeling package Quantum Espresso on Google Colab.
mat2vec - Supplementary Materials for Tshitoyan et al. "Unsupervised word embeddings capture latent knowledge from materials science literature", Nature (2019).
BestPractices - Things that you should (and should not) do in your Materials Informatics research.
ViTGAN - A PyTorch implementation of ViTGAN based on paper ViTGAN: Training GANs with Vision Transformers.
material-design-icons - Material Design icons by Google (Material Symbols)
GAT - Graph Attention Networks (https://arxiv.org/abs/1710.10903)
MaterialDesign - ✒7000+ Material Design Icons from the Community
Invariant-Attention - An implementation of Invariant Point Attention from Alphafold 2
Perceiver - Implementation of Perceiver, General Perception with Iterative Attention