Combining generative modeling and genetic algorithm for atomistic structure search

We developed a multi-objective genetic algorithm, FANTASTX (Fully Automated Nanoscale to Atomistic Structures from Theory and eXperiment) software, where we combined the theoretical tools with experimental data to determine the atomistic structure of experimentally observed materials. This work enhances the FANTASTX software with generative models to discover new candidate structures. We use variational autoencoders (VAE) and generative adversarial networks (GAN) to generate low-energy structures by training the models with system specific data. By combining the generative models with a genetic algorithm, we effectively sample local minima from regions with known data while exploring the potential energy landscape with genetic operations. Further, we train a crystal graph network to predict the formation free energies of the newly generated structures with the same training data. We implement different structure representation methods in the FANTASTX framework to test its effectiveness. We test the accuracy of the reconstruction of various examples from x-ray and electron microscopy data with different structure representation methods.