A data-driven approach is used to describe the growth of crystals and thin films based on high-throughput numerical experiments. This way the Machine Learning (ML) helps to reveal hidden patterns in the results from our atomistic-scale model which combines Cellular Automaton and Monte-Carlo moduli shaped as a Kossel crystal, through a carefully devised monitoring scheme, i.e. our computational nanoscope. Thus, from the quantities monitored in real-time, like growth rate and surface roughness, evolves the powerful concept of a crystal growth kernel — an ML-based comprehensive digital library that describes the growth of a crystal adequately across all the chosen ranges of system parameters, sizes, from atomistic to macroscopic, and growth modes — step-flow growth and instabilities, nucleation, rough growth, etc. Here we start with the analysis of how the kernel's prediction accuracy and time depend on the number of parameters.

中文翻译：

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计算机结晶的数据科学


基于高通量数值实验，使用数据驱动的方法来描述晶体和薄膜的生长。通过这种方式，机器学习 （ML） 有助于揭示我们的原子尺度模型结果中的隐藏模式，该模型通过精心设计的监控方案（即我们的计算纳米镜）结合了元胞自动机和形状为 Kossel 晶体的蒙特卡洛模量。因此，从实时监测的量（如生长速率和表面粗糙度）中，演变出晶体生长内核的强大概念——一个基于 ML 的综合数字库，它充分描述了晶体在所有选定的系统参数、尺寸范围内（从原子到宏观）和生长模式的生长——阶跃流生长和不稳定性、成核、 粗糙的生长等。在这里，我们首先分析内核的预测准确性和时间如何取决于参数的数量。