In recent years, diffusion-based models have demonstrated exceptional performance in searching for simultaneously stable, unique, and novel (S.U.N.) crystalline materials. However, most of these models don't have the ability to change the number of atoms in the crystal during the generation process, which limits the variability of model sampling trajectories. In this paper, we demonstrate the severity of this restriction and introduce a simple yet powerful technique, mirage infusion, which enables diffusion models to change the state of the atoms that make up the crystal from existent to non-existent (mirage) and vice versa. We show that this technique improves model quality by up to x2.5 compared to the same model without this modification. The resulting model, Mirage Atom Diffusion (MiAD), is an equivariant joint diffusion model for de novo crystal generation that is capable of altering the number of atoms during the generation process. MiAD achieves an 8.2% S.U.N. rate on the MP-20 dataset, which substantially exceeds existing state-of-the-art approaches. Code: https://github.com/andrey-okhotin/miad.git

翻译：近年来，基于扩散的模型在同时搜索稳定、独特且新颖（S.U.N.）的晶体材料方面表现出卓越性能。然而，大多数此类模型在生成过程中无法改变晶体中的原子数量，这限制了模型采样轨迹的多样性。本文中，我们论证了这一限制的严重性，并引入了一种简单而强大的技术——幻影注入（mirage infusion），该技术使扩散模型能够将组成晶体的原子状态从存在变为不存在（幻影），反之亦然。我们证明，与未经此修改的相同模型相比，该技术可将模型质量提升高达2.5倍。所得模型MiAD（Mirage Atom Diffusion）是一种用于从头晶体生成的等变联合扩散模型，能够在生成过程中改变原子数量。MiAD在MP-20数据集上实现了8.2%的S.U.N.率，显著超越了现有最优方法。代码：https://github.com/andrey-okhotin/miad.git