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.

利用潜在空间贝叶斯优化以及变分自编码器 (VAE) 相结合的方法，通过使用多个 VAE 以及整合模型来简化学习过程和增加探索性，我们的研究开创了利用潜在空间贝叶斯优化来进行全新晶体设计的方法，并通过主要关注形成能量值的优化任务来展示了该方法的有效性，为全新晶体发现提供了一种新的视角。