Dataset for the paper 'Predicting mechanical properties of polycrystalline nanopillars by interpretable machine learning'

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Dataset for the paper 'Predicting mechanical properties of polycrystalline nanopillars by interpretable machine learning'

Description

This dataset contains the data produced for the above paper. The dataset consists of: - input nanopillars before deformation (molecular_dynamics/nanopillars) - stress-strain curves acquired by deforming the nanopillars (molecular_dynamics/stress_strain_curves) - weights of the CNNs trained to predict mechanical properties of the nanopillars (machine_learning/train_CNN) - Grad-CAM fields of the predictions (machine_learning/train_CNN) Codes used for creating and analyzing the dataset are available at https://github.com/tekoivisto/nanopillar-ML
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Year of publication

2024

Authors

Tampere University

Lasse Laurson - Creator

Marcin Minkowski - Creator

Unknown organization

Teemu Koivisto - Creator

Zenodo - Publisher

Other information

Fields of science

Physical sciences; Electronic, automation and communications engineering, electronics

Language

English

Open access

Open

License

Creative Commons Attribution 4.0 International (CC BY 4.0)

Keywords

Physical sciences, Electronic automation and communications engineering electronics