Abstract
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.
Highlights
Machine learning is playing an increasing role in the discovery of new materials and may facilitate the search for optimum growth conditions for crystals and thin films
Our work demonstrates how artificial intelligence methods can help to unravel microscopic details of nonequilibrium surface processes that are crucially important during thin-film growth
The experimentally obtained value of nx can be used to start an elaborate fitting procedure employing kinetic Monte-Carlo (KMC) simulations to find the energy barriers that lead to island densities matching the experiment[30]
Summary
Machine learning is playing an increasing role in the discovery of new materials and may facilitate the search for optimum growth conditions for crystals and thin films. 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. In a few recent studies, machine learning has already been suggested in the spirit of a phenomenological optimization, e.g., to count the number of nucleation events and crystals[16], as well as for phenomenological optimization of process parameters from real-time datasets[17] These studies have indicated that machine-learning techniques can help to handle the significant challenge of finding optimum growth conditions. One possibility to determine energy barriers is via atomistic nucleation theory, which predicts scaling relations, e.g., for the maximum of the itselmanpderdaetunrseit2y3–n26x
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