基于小样本数据的晶体合成工艺智能推荐研究

doi:10.11871/jfdc.issn.2096-742X.2026.01.018

数据与计算发展前沿 ›› 2026, Vol. 8 ›› Issue (1): 219-231.

CSTR: 32002.14.jfdc.CN10-1649/TP.2026.01.018

doi: 10.11871/jfdc.issn.2096-742X.2026.01.018

• 技术与应用 • 上一篇

基于小样本数据的晶体合成工艺智能推荐研究

朱冬^1,²(),杨小渝^1,^2,^*(),唐述杰^2,³,朱锋锋^2,³,孔潇^2,³,郭艳峰^4,⁵,李兵⁶,秦志鹏⁶

1.中国科学院计算机网络信息中心，北京 100083
2.中国科学院大学，北京 100049
3.中国科学院上海微系统与信息技术研究所，上海 200050
4.上海科技大学，物质科学与技术学院，上海 201210
5.上海科技大学，拓扑物理实验室，上海 201210
6.华为技术有限公司，广东东莞 523808

收稿日期:2025-03-27 出版日期:2026-02-20 发布日期:2026-02-02
通讯作者: 杨小渝
作者简介:朱冬，中国科学院计算机网络信息中心，博士研究生，专业方向为计算机应用技术，目前主要研究方向为人工智能在材料科学领域的应用。
本文中负责模型整体架构设计、实现和评估，完成论文初稿撰写。
ZHU Dong is a Ph.D. candidate at the Computer Network Information Center, Chinese Academy of Sciences, majoring in Computer Application Technology. His current research focuses on the application of artificial intelligence in the field of materials science.
In this paper, he was responsible for the overall model architecture design, implementation, and evaluation, as well as drafting the initial version of the manuscript.
E-mail: zhudong@cnic.cn|杨小渝，中国科学院计算机网络信息中心，研究员，博士，英国剑桥大学博士后，中国科学院“百人计划”引进人才。目前主要研究方向为高通量材料集成计算、多尺度模拟计算、和AI驱动的新材料研发。
在本文中提出了方法与思路，制定论文框架，论文指导和修改。
YANG Xiaoyu, Ph.D., completed his postdoctoral research at the University of Cambridge, UK, and is currently a Researcher at the Computer Network Information Center, Chinese Academy of Sciences. He was recruited through the "Hundred Talents Program" of the Chinese Academy of Sciences. His main research interests include high-throughput integrated computational materials science, multiscale simulation, and AI-driven new material development.
In this paper, he proposed the idea and participated in formulating the framework, provided guidance, and revised the manuscript.
E-mail: kxy@cnic.cn
基金资助:
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);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);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)

Research on An Intelligent Recommendation Model for Crystal Synthesis Procedures Based on Small-Sample Data

ZHU Dong^1,²(),YANG Xiaoyu^1,^2,^*(),TANG Shujie^2,³,ZHU Fengfeng^2,³,KONG Xiao^2,³,GUO Yanfeng^4,⁵,LI Bing⁶,QIN Zhipeng⁶

1. Computer Network Information Center, Chinese Academy of Sciences, Beijing 100083, China
2. University of Chinese Academy of Sciences, Beijing 100049, China
3. Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 200050, China
4. School of Physical Science and Technology, ShanghaiTech University, Shanghai 201210, China
5. Laboratory for Topological Physics, ShanghaiTech University, Shanghai 201210, China
6. Huawei Technologies Co., Ltd., Dongguan, Guangdong 523808, China

Received:2025-03-27 Online:2026-02-20 Published:2026-02-02
Contact: YANG Xiaoyu

摘要/Abstract

摘要：

【背景】晶体合成是制备新材料的关键环节，但工艺条件复杂多变，实验数据稀缺，工艺确定困难。【问题】如何基于有限的“结构-工艺”数据实现晶体合成工艺的智能生成与可行性评估，是当前亟待解决的问题。【方法】本文基于晶体结构理解增强模型CrysBert，构建了适用于小样本的结构-工艺可行性判别模型；基于晶体结构生成模型CrysGPT，构建了晶体合成工艺生成模型；通过CrysBert和CrysGPT的协同，实现晶体工艺的自动生成与筛选。【结果】基于162条小样本数据训练，工艺判别模型准确率达到0.90，明显优于传统方法；工艺生成模型推荐的候选工艺，经判别模型评价，可行工艺比例达到60.7%，与领域专家推荐工艺的成功率（62.3%）接近。【结论】本研究验证了通过CrysBert和CrysGPT的协同，可为晶体合成工艺的智能设计提供新途径。

关键词: CrysBert, CrysGPT, 工艺智能推荐, 小样本数据, 晶体工艺设计

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 processes from limited “structure-process” datasets is urgently needed. [Methods] We constructed a feasibility evaluation 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 automatic 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 produced 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 provides a promising new pathway for intelligent crystal synthesis design.

Key words: CrysBert, CrysGPT, crystal synthesis process intelligent recommendation, small-sample data, crystal synthesis process design

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.

ZHU Dong,YANG Xiaoyu,TANG Shujie,ZHU Fengfeng,KONG Xiao,GUO Yanfeng,LI Bing,QIN Zhipeng. Research on An Intelligent Recommendation Model for Crystal Synthesis Procedures Based on Small-Sample Data[J]. Frontiers of Data and Computing, 2026, 8(1): 219-231, https://cstr.cn/32002.14.jfdc.CN10-1649/TP.2026.01.018.

图/表 10

图1

表1

工艺数据展示"

材料名称	生长方法	元素比例	生长温度曲线和加热后操作	单晶生长结果
Ce₃Pt₃Bi₄	干烧	Ce $∶$ Pt $∶$ Bi=3 $∶$ 3 $∶$ 4	从30 ℃以15小时升温至1,100 ℃，保持20小时，再用100小时降温至800 ℃后离心	获得极小单晶颗粒

表1

图2

表2

图3

图4

表3

工艺数据示例"

材料名称	生长方法	元素比例	生长温度曲线和加热后操作	单晶生长结果
Ce₃Pt₃Bi₄	干烧	Ce $∶$ Pt $∶$ Bi=3 $∶$ 3 $∶$ 4	从30 ℃以15小时升温至1,100 ℃，保持20小时…	获得极小单晶颗粒
Ca2Ge	干烧	Ca $∶$ Ge=13 $∶$ 7	从30 ℃用11小时升温到1,050 ℃，维持20小时…	未成功
$?$	$?$	$?$	$?$	$?$
PrAlGe	助熔剂	Pr $∶$ Ga $∶$ Ge $∶$ In=1 $∶$ 1 $∶$ 1 $∶$ 20	从30 ℃用20小时升温1,150 ℃，维持15小时…	生长出单晶

表3

表4

图5

图6

参考文献 27

[1]	QI R, ZHU B, HAN Z, et al. High-Throughput Screening of Stable Single-Atom Catalysts in CO2 Reduction Reactions[J]. ACS Catalysis, 2022, 12(14): 8269-8278. doi: 10.1021/acscatal.2c02149
[2]	ZHANG B, ABBAS A, ROMAGNOLI J A. Monitoring crystal growth based on image texture analysis using wavelet transformation[J]. IFAC Proceedings Volumes, 2012, 45(15): 33-38.
[3]	ASKARI S, BASHARDOUST SIAHMARD A, HALLADJ R, et al. Different techniques and their effective parameters in nano SAPO-34 synthesis: A review[J]. Powder Technology, 2016, 301: 268-287. doi: 10.1016/j.powtec.2016.06.018
[4]	RAWLINGS J B, MILLER S M, WITKOWSKI W R. Model identification and control of solution crystallization processes: a review[J]. Industrial & Engineering Chemistry Research, 1993, 32(7): 1275-1296. doi: 10.1021/ie00019a002
[5]	ROSSO V W, YIN Z, ABOURAHMA H, et al. High-Throughput Crystallization Screening Technique with Transmission PXRD Analysis[J]. Organic Process Research & Development, 2023, 27(8): 1437-1444.
[6]	BAIRD S G, LIU M, SAYEED H M, et al. Data-Driven Materials Discovery and Synthesis using Machine Learning Methods[M]. Comprehensive Inorganic Chemistry III (Third Edition), 2023: 3-23.
[7]	谢建新, 宿彦京, 薛德祯, 等. 机器学习在材料研发中的应用[J]. 金属学报, 57(11): 1343-1361.
[8]	HIMANEN L, GEURTS A, FOSTER A S, et al. Data-Driven Materials Science: Status, Challenges, and Perspectives[J]. Advanced Science, 2019, 6(21): 1900808. doi: 10.1002/advs.v6.21
[9]	MERCHANT A, BATZNER S, SCHOENHOLZ S S, et al. Scaling deep learning for materials discovery[J]. Nature, 2023, 624(7990): 80-85. doi: 10.1038/s41586-023-06735-9
[10]	ZHU D, XIN Z, ZHENG S, et al. Addressing the Accuracy-Cost Trade-off in Material Property Prediction Using a Teacher-Student Strategy[J]. Journal of Chemical Theory and Computation, 2024, 20(13): 5743-5750. doi: 10.1021/acs.jctc.4c00625
[11]	ZHU D, WANG C, ZOU P, et al. Deep-Learning Aided Atomic-Scale Phase Segmentation toward Diagnosing Complex Oxide Cathodes for Lithium-Ion Batteries[J]. Nano Letters, 2023, 23(17): 8272-8279. doi: 10.1021/acs.nanolett.3c02441
[12]	SONG Z, LU S, JU M, et al. Is Large Language Model All You Need to Predict the Synthesizability and Precursors of Crystal Structures?[EB/OL]. arXiv, 2024.
[13]	MYERSON A S, ERDEMIR D, LEE A Y. Handbook of Industrial Crystallization[M]. 3rd ed. Cambridge: Cambridge University Press, 2019: 249-266.
[14]	JHA D, CHOUDHARY K, TAVAZZA F, et al. Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning[J]. Nature Communications, 2019, 10(1): 5316. doi: 10.1038/s41467-019-13297-w pmid: 31757948
[15]	FISHER O J, WATSON N J, ESCRIG J E, et al. Considerations, challenges and opportunities when developing data-driven models for process manufacturing systems[J]. Computers & Chemical Engineering, 2020, 140: 106881. doi: 10.1016/j.compchemeng.2020.106881
[16]	LUO Y, BAG S, ZAREMBA O, et al. MOF Synthesis Prediction Enabled by Automatic Data Mining and Machine Learning[J]. Angewandte Chemie International Edition, 2022, 61(19): e202200242.
[17]	GRATTAFIORI A, DUBEY A, JAUHRI A, et al. The Llama 3 Herd of Models[EB/OL]. arXiv, 2024.
[18]	LU W, LUU R K, BUEHLER M J. Fine-tuning large language models for domain adaptation: Exploration of training strategies, scaling, model merging and synergistic capabilities[EB/OL]. arXiv, 2024.
[19]	MULLIN J W. 6-Crystal growth[M]// MULLINJ W. Crystallization (Fourth Edition). Oxford: Butterworth-Heinemann, 2001: 216-288.
[20]	DEVLIN J, CHANG M W, LEE K, et al. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding[EB/OL]. arXiv, 2019.
[21]	SUTSKEVER I, VINYALS O, LE Q V. Sequence to Sequence Learning with Neural Networks[C]// Advances in Neural Information Processing Systems: Vol. 27. Curran Associates, Inc., 2014.
[22]	WEI J, BOSMA M, ZHAO V Y, et al. Finetuned Language Models Are Zero-Shot Learners[EB/OL]. arXiv, 2022.
[23]	OUYANG L, WU J, JIANG X, et al. Training language models to follow instructions with human feedback[EB/OL]. arXiv, 2022.
[24]	SAHOO P, SINGH A K, SAHA S, et al. A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications[EB/OL]. arXiv, 2024.
[25]	LI X L, LIANG P. Prefix-Tuning: Optimizing Continuous Prompts for Generation[EB/OL]. arXiv, 2021.
[26]	DETTMERS T, PAGNONI A, HOLTZMAN A, et al. QLoRA: Efficient Finetuning of Quantized LLMs[EB/OL]. arXiv, 2023.
[27]	BREIMAN L. Random Forests[J]. Machine Learning, 2001, 45(1): 5-32. doi: 10.1023/A:1010933404324

工艺种类	预处理方法	向量长度描述
生长方法	采用one-hot	5维（干烧法、助熔剂法、气相传输法、复合法、固相反应法）
反应物	采用one-hot 以及数值型	Onehot 4×25维（4种元素）数值4维（元素比例）
反应温度	采用数值型	最多7个阶段，每阶段记录2个数值（温度和时长），共14维
加热后处理	采用one-hot	4维（离心、退火、淬火以及无操作）

模型	输入	判别准确率
SynBert	晶体结构+数据预处理工艺	0.90
CrysGPT微调判别	CIF文本+工艺文本	0.88
工艺神经网络	特征化晶体结构+数据预处理工艺	0.78
随机森林	特征化晶体结构+数据预处理工艺	0.75

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

RichHTML

PDF

可视化

摘要/Abstract

引用本文

使用本文

图/表 10

参考文献 27

相关文章 0

编辑推荐

Metrics

本文评价