Efficient Sampling for Structure Search Using VAE-Organized Latent Spaces and Genetic Algorithms

Structure search through evolutionary algorithm methods is well established for novel materials discovery in a specific chemical system. The evolutionary search typically occurs in the structure space, which is not necessarily organized according to desired properties such as stability or band gap. To address this, we have developed an extension to our in-house multi-objective evolutionary algorithm package, FANTASTX (Fully Automated Nanoscale To Atomistic Structure from Theory and eXperiments). The updated framework initially performs the genetic algorithm-based structure search and uses the generated data to train a variational autoencoder (VAE). We use an auxiliary network to organize the VAE latent space according to a desired property, which can be rapidly predicted by an independent, trained surrogate model. We sample new structures from the latent space and evaluate them using density functional theory (DFT) or force fields. In this talk, we will discuss the performance of the generative and evolutionary synergistic framework based on the tests performed on the CdTe system using force fields and the IrOx system using DFT calculations.