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>
ShareScore
32/100
Overall dataset sharing score
Score breakdown
These five areas show where the dataset supports — or may limit — practical reuse.
- Stewardship
- 4
- Harmonization
- 4
- Access
- 16
- Reuse readiness
- 8
- Engagement
- 0