Seda Oturak

data-resources-for-materials-science. A list of databases, datasets and books/handbooks where you can find materials properties for machine learning applications.

Quantum_Espresso_Colab. This repository includes a notebook to run the open-source materials modeling package Quantum Espresso on Google Colab.

vae_cahn-hilliard. Generative model variational autoencoder (VAE) implementation for the predictions of phase separation in binary alloys

linear_elasticity_3D_fenics. Finite element modeling for linear elasticity problem in 3D by using FEniCS software

normalization-frc. Simple desktop application for normalization of tensile/compression test data of fiber reinforced composites

dcgan_cahn-hilliard. Generative model Deep Convolutional Generative Adversarial Networks (DCGAN) implementation for the predictions of phase separation in binary alloys

ScienceU. Material simulation tutorial on DFT for high school students to find alternative, green energy source to fossil fuels.

simple_digital_twin_in_materials_science. Simple Digital Twin example in materials science

OC22_adsorption_energy. Notebook for calculating adsorption energy from the total energies in OC22 dataset.

tricks_for_efficient_pytorch. Some tricks to speed up the codes written with PyTorch library.