提出可增减原子数的晶体生成模型，提升新物质发现效率。
MiAD: Mirage Atom Diffusion for De Novo Crystal Generation
- 引入'幻影注入'技术，让扩散模型自由增删原子
- 在MP-20数据集上实现8.2%的S.U.N.率，性能提升2.5倍
- 适合材料科学领域的新结构设计与生成任务
近年来，基于扩散的模型在搜索同时具备稳定性、唯一性和新颖性的晶体材料（S.U.N.）方面表现卓越。然而，多数模型在生成过程中无法改变晶体中原子数量，限制了采样轨迹的多样性。本文揭示了这一限制的严重性，并提出一种简单而有效的技术——幻影注入，使扩散模型能在存在与不存在之间动态调整原子状态。实验表明，该技术可使模型性能提升最高达2.5倍。由此构建的镜像原子扩散模型（MiAD）是一种等变联合扩散模型，能动态调节原子数量。在MP-20数据集上，MiAD实现了8.2%的S.U.N.率，显著超越现有最先进方法。
原文摘要 · Abstract (English)
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
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