Machine learning is playing an increasing role in the discovery of new materials and may also facilitate the search for optimum growth conditions for crystals and thin films. Here, we perform kinetic Monte-Carlo simulations of sub-monolayer growth. We consider a generic homoepitaxial growth scenario that covers a wide range of conditions with different diffusion barriers (0.4–0.55 eV) and lateral binding energies (0.1–0.4 eV). These simulations are used as a training data set for a convolutional neural network that can predict diffusion barriers and binding energies. Specifically, a single Monte-Carlo image of the morphology is sufficient to determine the energy barriers with an accuracy of approximately 10 meV and the neural network is tolerant to images with noise and lower than atomic-scale resolution. We believe this new machine learning method will be useful for fundamental studies of growth kinetics and growth optimization through better knowledge of microscopic parameters.

机器学习在发现新材料方面发挥着越来越重要的作用，也可能有助于寻找晶体和薄膜的最佳生长条件。在这里，我们对亚单层生长进行动力学蒙特卡罗模拟。我们考虑了一个通用的同质外延生长场景，它涵盖了具有不同扩散势垒（0.4-0.55 eV）和横向结合能（0.1-0.4 eV）的各种条件。这些模拟被用作卷积神经网络的训练数据集，可以预测扩散障碍和结合能。具体而言，形态的单个蒙特卡罗图像足以以大约 10 meV 的精度确定能量势垒，并且神经网络可以容忍具有噪声且低于原子级分辨率的图像。