基于机器学习的光学功能玻璃研究进展
Research Progress of Optical Functional Glass Based on Machine Learning
付丽丽 1张志强 1徐慧敏 1任青颖 1郑锐林 2韦玮1
作者信息
- 1. 南京邮电大学电子与光学工程学院、柔性电子(未来技术)学院,江苏 南京 210023
- 2. 南京邮电大学电子与光学工程学院、柔性电子(未来技术)学院,江苏 南京 210023;南京邮电大学理学院,江苏 南京 210023
- 折叠
摘要
光功能玻璃材料在研究过程中所涉及到的研发周期长和效率低的问题,极大阻碍了光学玻璃材料的发展.机器学习技术的出现对玻璃材料学发展起到了极大的推动作用,通过学习数据蕴含的规律,在庞大而复杂的玻璃数据中学习并预测新数据,加快了光学功能玻璃的研发进程.本文总结并展示了在光学玻璃预测中涉及到的几类机器学习算法并对其做了简要介绍,在此基础上重点介绍这些理论算法在玻璃研究工作中的重要应用,包括加速和改进传统玻璃研究方法、助力玻璃的成分-性质关联预测以及对光学玻璃的配方设计的建议等方面的内容,最后对机器学习在光功能玻璃研究中的应用前景以及未来发展趋势做了分析与展望.
Abstract
The research process of optical functional glass materials involves long research and development cycles and low efficiency.Greatly hindered the development of optical glass materials.The emergence of machine learning technology has greatly promoted the development of glass materials science.By learning the laws contained in the data,learning and predicting new data from the huge and complex glass data has accelerated the research and development process of optical functional glass.This paper summarizes and demonstrates several types of machine learning algorithms involved in the prediction of optical glass and briefly introduces them.On this basis,it focuses on summarizing the important applications of these theoretical algorithms in glass research,including accelerating and improving traditional glass research methods,assisting glass composition-property correlation prediction,and suggestions for optical glass formulation design.Finally,the application prospects and future development trends of machine learning in optical functional glass research are analyzed and forecasted.
关键词
神经网络/机器学习/光功能玻璃/性质-成分关联/性能预测Key words
neural network/machine learning/optical functional glass/property-composition correlation/performance prediction引用本文复制引用
出版年
2024
NETL
NSTL
万方数据