Crystal-LSBO: Automated Design of De Novo Crystals with Latent Space Bayesian Optimization

TL;DR

This paper introduces Crystal-LSBO, a novel framework combining multiple VAEs and Latent Space Bayesian Optimization to facilitate the de novo design of crystal structures, overcoming data complexity challenges.

Contribution

Crystal-LSBO is the first to apply LSBO to crystal design, using multiple VAEs to simplify learning and improve exploration of crystal structures.

Findings

Effective optimization of formation energy values

Enhanced exploration of crystal latent spaces

Pioneering use of LSBO in crystal discovery

Abstract

Generative modeling of crystal structures is significantly challenged by the complexity of input data, which constrains the ability of these models to explore and discover novel crystals. This complexity often confines de novo design methodologies to merely small perturbations of known crystals and hampers the effective application of advanced optimization techniques. One such optimization technique, Latent Space Bayesian Optimization (LSBO) has demonstrated promising results in uncovering novel objects across various domains, especially when combined with Variational Autoencoders (VAEs). Recognizing LSBO's potential and the critical need for innovative crystal discovery, we introduce Crystal-LSBO, a de novo design framework for crystals specifically tailored to enhance explorability within LSBO frameworks. Crystal-LSBO employs multiple VAEs, each dedicated to a distinct aspect of crystal structure: lattice, coordinates, and chemical elements, orchestrated by an integrative model that synthesizes these components into a cohesive output. This setup not only streamlines the learning process but also produces explorable latent spaces thanks to the decreased complexity of the learning task for each model, enabling LSBO approaches to operate. Our study pioneers the use of LSBO for de novo crystal design, demonstrating its efficacy through optimization tasks focused mainly on formation energy values. Our results highlight the effectiveness of our methodology, offering a new perspective for de novo crystal discovery.