光电效应遏止电压神经网络预测研究
Neural Network-Based Prediction of Stopping Voltage in the Photoelectric Effect

光电效应是现代物理学发展过程中的重要物理现象，其揭示了光的粒子性，并为量子理论的建立提供了重要实验基础。传统光电效应分析主要依赖爱因斯坦光电效应方程，通过入射光频率、金属逸出功等物理量计算光电子最大初动能和遏止电压。随着人工智能方法的发展，神经网络在非线性函数逼近、物理建模和快速预测方面表现出较强能力。为探索神经网络在现代物理理论计算中的应用价值，本文以光电效应理论为基础，构建由入射光频率、金属逸出功和相对光强组成的理论数值数据集，并建立前馈神经网络模型，对遏止电压和光电效应发生状态进行预测。研究首先依据光子能量公式和爱因斯坦光电效应方程生成可复现的理论计算数据，然后利用神经网络学习物理变量之间的映射关系，最后通过理论曲线、预测对比、误差评价和混淆矩阵对模型性能进行分析。结果表明，神经网络能够较好逼近光电效应理论关系，对遏止电压具有较高预测精度，同时能够有效判断入射光是否满足光电效应发生条件。本文研究说明，在明确物理理论依据和数据来源的前提下，神经网络可以作为现代物理理论计算、规律可视化和快速预测的有效辅助工具。
The photoelectric effect is an important physical phenomenon in the development of modern physics. It reveals the particle nature of light and provides an essential foundation for quantum theory. Conventional analysis of the photoelectric effect mainly relies on Einstein’s photoelectric equation, in which the maximum kinetic energy and stopping voltage of photoelectrons are calculated from the incident light frequency and the work function of metals. With the development of artificial intelligence, neural networks have shown strong capabilities in nonlinear function approximation, physical modeling and rapid prediction. To explore the application value of neural networks in theoretical calculation of modern physics, this paper constructs a reproducible theoretical numerical dataset based on the photoelectric effect theory. A feedforward neural network is established to predict the stopping voltage and the occurrence state of the photoelectric effect. The theoretical data are generated according to the photon energy formula and Einstein’s photoelectric equation. The model performance is evaluated through theoretical curves, prediction comparison, error metrics and a confusion matrix. The results show that the neural network can effectively approximate the theoretical relationship of the photoelectric effect and correctly identify whether the photoelectric effect occurs under given physical conditions. This study indicates that neural networks can serve as useful auxiliary tools for theoretical computation, visualization and rapid prediction in modern physics when the data source and physical constraints are clearly defined.