数据与计算发展前沿2026，Vol.8Issue(1)：219-231,13.DOI:10.11871/jfdc.issn.2096-742X.2026.01.018
基于小样本数据的晶体合成工艺智能推荐研究
Research on An Intelligent Recommendation Model for Crystal Synthesis Procedures Based on Small-Sample Data
摘要
Abstract
[Background]Crystal synthesis is vital for developing new materials but challenging due to complex,variable conditions and limited experimental data.[Problem]Intelligently generating and evaluating crystal synthesis pro-cesses from limited"structure-process"datasets is urgently needed.[Methods]We constructed a feasibility evalu-ation model based on the structure-understanding model(CrysBert)optimized for small datasets,and a generative synthesis model utilizing the large structure-generation model(CrysGPT).Integrating both models enabled auto-matic process generation and screening.[Results]Trained on 162 small-sample data points,the discriminative model achieved an accuracy of 0.90,outperforming traditional methods.The generative model efficiently pro-duced candidate processes,achieving a feasibility rate of 60.7%after discriminative screening,approaching the expert benchmark(62.3%).[Conclusions]This study demonstrates that integrating CrysBert and CrysGPT pro-vides a promising new pathway for intelligent crystal synthesis design.关键词
CrysBert/CrysGPT/工艺智能推荐/小样本数据/晶体工艺设计Key words
CrysBert/CrysGPT/crystal synthesis process intelligent recommendation/small-sample data/crystal synthesis process design引用本文复制引用
朱冬,杨小渝,唐述杰,朱锋锋,孔潇,郭艳峰,李兵,秦志鹏..基于小样本数据的晶体合成工艺智能推荐研究[J].数据与计算发展前沿,2026,8(1):219-231,13.基金项目
国家自然科学基金面上项目"生成式AI驱动的高分子智能设计方法与技术研究"(62376258) （62376258）
国家自然科学基金联合基金项目"人工智能驱动的海洋防腐涂层材料按需逆向设计"(U24B20126) （U24B20126）
云南省重点研发计划-材料基因工程项目"高强塑积、轻质高强韧耐磨钢及其大型耐磨件关键成形技术研发(202403AA08001) （202403AA08001）
