TY - JOUR
T1 - A Large-Scale Nanocrystal Database with Aligned Synthesis and Properties, Enabling Generative Inverse Design
AU - Gu, Kai
AU - Liang, Yingping
AU - Peng, Senliang
AU - Guo, Aotian
AU - Fu, Ying
AU - Zhong, Haizheng
N1 - Publisher Copyright:
© 2026 American Chemical Society
PY - 2026/6/23
Y1 - 2026/6/23
N2 - Nanocrystal synthesis 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 data sets that align nanocrystal synthesis routes with their properties. In this work, we developed NanoExtractor, a large language model (LLM) with well-designed data augmentation strategies to extract structured synthesis routes and corresponding properties from unstructured literature. NanoExtractor achieves a weighted average score of 92% on the test set, significantly outperforming other chemistry-specialized (9%) and general-purpose LLMs (57%). With this model, we constructed a large-scale nanocrystal synthesis-property (NSP) database containing nearly 160 000 aligned entries. On the basis of this database, we further developed NanoDesigner, an LLM for generative inverse synthesis design, achieving an F1 score of 0.85. The applicability of NanoDesigner was experimentally validated across multiple nanocrystal systems, including MgF2, CsPbBr3, and PbS. Notably, NanoDesigner recommends a critical nonstoichiometric precursor concentration for synthesizing MgF2 nanocrystals, which was experimentally proven to be essential for suppressing byproduct formation. In all, our work bridges the gap between unstructured literature and data-driven synthesis, providing a human-AI collaborative paradigm for accelerating material discovery.
AB - Nanocrystal synthesis 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 data sets that align nanocrystal synthesis routes with their properties. In this work, we developed NanoExtractor, a large language model (LLM) with well-designed data augmentation strategies to extract structured synthesis routes and corresponding properties from unstructured literature. NanoExtractor achieves a weighted average score of 92% on the test set, significantly outperforming other chemistry-specialized (9%) and general-purpose LLMs (57%). With this model, we constructed a large-scale nanocrystal synthesis-property (NSP) database containing nearly 160 000 aligned entries. On the basis of this database, we further developed NanoDesigner, an LLM for generative inverse synthesis design, achieving an F1 score of 0.85. The applicability of NanoDesigner was experimentally validated across multiple nanocrystal systems, including MgF2, CsPbBr3, and PbS. Notably, NanoDesigner recommends a critical nonstoichiometric precursor concentration for synthesizing MgF2 nanocrystals, which was experimentally proven to be essential for suppressing byproduct formation. In all, our work bridges the gap between unstructured literature and data-driven synthesis, providing a human-AI collaborative paradigm for accelerating material discovery.
KW - inverse design
KW - large language model
KW - literature extraction
KW - nanocrystal synthesis
KW - quantum dots
UR - https://www.scopus.com/pages/publications/105042523019
U2 - 10.1021/acsnano.6c03070
DO - 10.1021/acsnano.6c03070
M3 - Article
AN - SCOPUS:105042523019
SN - 1936-0851
VL - 20
SP - 17413
EP - 17422
JO - ACS Nano
JF - ACS Nano
IS - 24
ER -