Machine Learning-Based Prediction of the Excitation Wavelength of Phosphors

IF 0.8 4区 化学 Q4 SPECTROSCOPY
Sunil K. Sahu, Anil Shrivastav, N. K. Swamy, Vikas Dubey, D. K. Halwar, M. Tanooj Kumar, M. C. Rao
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引用次数: 0

Abstract

Current challenges in the field of luminescent materials are concerned with designing efficient material to meet the rapidly rising demands of industry. Luminescent material excitation and emission are highly complex phenomena driven by the combination of atomic-level properties such as valence electron, inter-atomic radius, ionic radius, etc., and physical properties such as crystal structure, symmetry, etc. The current research paper focuses on the development of a machine-learning algorithm based on simple luminescent materials to predict the excitation to the closest possible accuracy using easily accessible key attributes by the CatBoost regressor, multiple linear regression (MLR), and an artificial neural network (ANN) approach. These selected features likely correlate with the excitation of the material. In comparison, the ANN and MLR algorithms have higher mean absolute error values in both the training and test datasets. The CatBoost algorithm outperforms the other algorithms in terms of mean of the absolute percentage difference, achieving a value of 0.302136% in the training dataset. The CatBoost algorithm exhibits the lowest root mean squared error value of 1.680768 nm in the training dataset, indicating that its predictions have a smaller average deviation from the actual values. The style for studying the material property has the potential to reduce the cost and time involved in an Edisonian approach to the lengthy laboratory experiment to identify excitation.

基于机器学习的荧光粉激发波长预测
当前,发光材料领域面临的挑战是如何设计出高效的材料,以满足快速增长的工业需求。发光材料的激发和发射是非常复杂的现象,由原子价电子、原子间半径、离子半径等原子级特性和晶体结构、对称性等物理特性共同驱动。本研究论文的重点是开发一种基于简单发光材料的机器学习算法,通过 CatBoost 回归器、多元线性回归(MLR)和人工神经网络(ANN)方法,利用易于获取的关键属性,尽可能准确地预测激发。这些选定的特征可能与材料的激发相关。相比之下,人工神经网络和多元线性回归算法在训练和测试数据集中的平均绝对误差值都较高。CatBoost 算法在绝对百分比差的平均值方面优于其他算法,在训练数据集中达到了 0.302136%。在训练数据集中,CatBoost 算法的均方根误差值最低,为 1.680768 nm,表明其预测值与实际值的平均偏差较小。这种研究材料特性的方法有可能减少爱迪生式方法所涉及的成本和时间,从而缩短确定激励的漫长实验室实验。
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来源期刊
CiteScore
1.30
自引率
14.30%
发文量
145
审稿时长
2.5 months
期刊介绍: Journal of Applied Spectroscopy reports on many key applications of spectroscopy in chemistry, physics, metallurgy, and biology. An increasing number of papers focus on the theory of lasers, as well as the tremendous potential for the practical applications of lasers in numerous fields and industries.
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