用生成模型加速高温超导体发现，自动生成数万候选结构。
Guided Diffusion for the Discovery of New Superconductors
- 基于扩散模型生成新材料结构，通过无分类器引导实现性质驱动设计。
- 筛选出773个理论超导转变温度高于5K的稳定候选材料。
- 适合材料设计、计算化学与超导研究方向的科研人员参考。
由于化学与结构空间庞大，实现具有特定性能（如高温超导性）的材料逆向设计极具挑战。本文提出一种引导扩散框架，以Alexandria数据库预训练的DiffCSP模型为基础，微调7,183个超导体的密度泛函理论标签。采用无分类器引导采样生成20万种结构，得到34,027个唯一候选。通过机器学习与密度泛函理论（DFT）结合的多阶段筛选，识别出773个具有DFT计算$T_ ext{c}>5$ K的候选材料。生成模型展现出有效性质驱动设计能力。本工作的计算结果经实验合成与表征验证，揭示了在稀疏化学区域中预测与合成的困难。该端到端流程加速了超导体发现，同时凸显了预测与可实现材料之间的挑战。
原文摘要 · Abstract (English)
The inverse design of materials with specific desired properties, such as high-temperature superconductivity, represents a formidable challenge in materials science due to the vastness of chemical and structural space. We present a guided diffusion framework to accelerate the discovery of novel superconductors. A DiffCSP foundation model is pretrained on the Alexandria Database and fine-tuned on 7,183 superconductors with first principles derived labels. Employing classifier-free guidance, we sample 200,000 structures, which lead to 34,027 unique candidates. A multistage screening process that combines machine learning and density functional theory (DFT) calculations to assess stability and electronic properties, identifies 773 candidates with DFT-calculated $T_\mathrm{c}>5$ K. Notably, our generative model demonstrates effective property-driven design. Our computational findings were validated against experimental synthesis and characterization performed as part of this work, which highlighted challenges in sparsely charted chemistries. This end-to-end workflow accelerates superconductor discovery while underscoring the challenge of predicting and synthesizing experimentally realizable materials.
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