High uniformity graphene has extensive application prospect in many important fields due to its excellent features. During large-area graphene synthesis by chemical vapor deposition, the optimization of the substrate temperature can improve the uniformity of graphene. Here, machine learning is used to design and optimize the substrate surface temperature for uniform graphene deposition. The computational fluid dynamics simulations based on a developed computational model are first performed to obtain the training data for machine learning, such as the gas temperature, velocity, concentrations, etc. Then, the neural network model is used to optimize the substrate temperature using the simulated data. It is found that the high accuracy is achieved through the validation of testing set. The optimal substrate temperature distribution is finally obtained, in which the carbon deposition rate and its uniformity are optimized to the specified values.

中文翻译：

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通过数值模拟和机器学习优化均匀石墨烯合成的基板温度

高均匀性石墨烯以其优异的特性在许多重要领域具有广泛的应用前景。在化学气相沉积大面积石墨烯合成过程中，优化基板温度可以提高石墨烯的均匀性。在这里，机器学习用于设计和优化基板表面温度，以实现均匀的石墨烯沉积。首先基于已开发的计算模型进行计算流体动力学模拟，以获得用于机器学习的训练数据，例如气体温度、速度、浓度等。然后，使用神经网络模型优化基板温度。模拟数据。发现通过测试集的验证实现了高精度。