matbench
Materials Science Benchmarking Tool
Provides tools and resources for testing machine learning performance on materials science data
Matbench: Benchmarks for materials science property prediction
132 stars
8 watching
47 forks
Language: Python
last commit: about 2 years agoLinked from 1 awesome list
benchmarkchemistrycondensed-matterdata-sciencemachine-learningmachine-learning-algorithmsmaterials-sciencephysics
tilde-lab/awesome-materials-informaticsRelated projects:
| Repository | Description | Stars |
|---|---|---|
 materialsvirtuallab/maml | A toolkit for machine learning in materials science, enabling the development of predictive models and simulations. | 376 |
 janosh/matbench-discovery | An evaluation framework for machine learning models used in materials discovery | 115 |
 ppdebreuck/modnet | A Python package implementing a machine learning framework for predicting material properties from composition or crystal structure data. | 82 |
 anthony-wang/crabnet | A deep learning framework for predicting material properties from composition information. | 94 |
| materialsproject/emmet | A framework for building and managing material properties databases | 55 |
| janosh/pymatviz | A toolkit for visualizing data in materials science and informatics | 177 |
 sparks-baird/matbench-genmetrics | Provides standardized benchmarks for evaluating the quality of generative models for crystal structures. | 34 |
 multimodal-art-projection/omnibench | Evaluates and benchmarks multimodal language models' ability to process visual, acoustic, and textual inputs simultaneously. | 15 |
 materialsintelligence/mat2vec | Unsupervised word embeddings capture latent knowledge from materials science literature | 624 |
 atomistic-machine-learning/schnetpack | A toolbox for training and applying deep neural networks to predict atomistic properties of molecules and materials | 795 |
 catboost/benchmarks | Comparative benchmarks of various machine learning algorithms | 169 |
 mlcommons/inference | Measures the performance of deep learning models in various deployment scenarios. | 1,256 |
| materialsproject/atomate2 | Automates complex materials science workflows using simple Python functions | 169 |
 szilard/benchm-ml | A benchmark for evaluating machine learning algorithms' performance on large datasets | 1,874 |
 usnistgov/jarvis | An open-source software package for data-driven materials design using atomistic simulations and machine learning techniques. | 318 |