大规模纳米晶体数据库：对齐合成与性质实现生成式逆向设计
A large-scale nanocrystal database with aligned synthesis and properties enabling generative inverse design
AI总结：
构建大规模对齐的纳米晶体合成-性质数据库，开发基于大语言模型的NanoExtractor提取文献数据，并利用NanoDesigner实现生成式逆向设计，成功设计PbSe和MgF2纳米晶体的合成路线。
摘要：
由于合成参数与物理化学性质之间的复杂相关性，纳米晶体的合成高度依赖于试错法。尽管深度学习为生成式逆向设计提供了潜在方法，但缺乏对齐纳米晶体合成路线与其性质的高质量数据集仍阻碍其发展。本文介绍了一个大规模、对齐的纳米晶体合成-性质（NSP）数据库的构建，并展示了其用于生成式逆向设计的能力。为了从文献中提取结构化的合成路线及其对应的产物性质，我们开发了NanoExtractor，这是一个通过精心设计的增强策略增强的大语言模型（LLM）。NanoExtractor经过人类专家验证，在测试集上达到88%的加权平均分，显著优于化学专用（3%）和通用LLM（38%）。生成的NSP数据库包含近16万条对齐条目，并作为我们的NanoDesigner（一个用于逆向合成设计的LLM）的训练数据。NanoDesigner的生成能力通过成功设计成熟的PbSe纳米晶体和罕见的MgF2纳米晶体的可行合成路线得到验证。值得注意的是，模型为MgF2纳米晶体推荐了反直觉的非化学计量前驱体比例（1:1），实验证实该比例对抑制副产物至关重要。我们的工作弥合了非结构化文献与数据驱动合成之间的差距，并建立了一个强大的人机协作范式，以加速纳米晶体的发现。
英文摘要：
The synthesis of nanocrystals has been highly dependent on trial-and-error, due to the complex correlation between synthesis parameters and physicochemical properties. Although deep learning offers a potential methodology to achieve generative inverse design, it is still hindered by the scarcity of high-quality datasets that align nanocrystal synthesis routes with their properties. Here, we present the construction of a large-scale, aligned Nanocrystal Synthesis-Property (NSP) database and demonstrate its capability for generative inverse design. To extract structured synthesis routes and their corresponding product properties from literature, we develop NanoExtractor, a large language model (LLM) enhanced by well-designed augmentation strategies. NanoExtractor is validated against human experts, achieving a weighted average score of 88% on the test set, significantly outperforming chemistry-specialized (3%) and general-purpose LLMs (38%). The resulting NSP database contains nearly 160,000 aligned entries and serves as training data for our NanoDesigner, an LLM for inverse synthesis design. The generative capability of NanoDesigner is validated through the successful design of viable synthesis routes for both well-established PbSe nanocrystals and rarely reported MgF2 nanocrystals. Notably, the model recommends a counter-intuitive, non-stoichiometric precursor ratio (1:1) for MgF2 nanocrystals, which is experimentally confirmed as critical for suppressing byproducts. Our work bridges the gap between unstructured literature and data-driven synthesis, and also establishes a powerful human-AI collaborative paradigm for accelerating nanocrystal discovery.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
