Thin films are ubiquitous in modern technology and highly useful in materials discovery and design. For achieving optimal extrinsic properties, their microstructure needs to be controlled in a multi-parameter space, which usually requires too high a number of experiments to map. Here, we propose to master thin film processing microstructure complexity, and to reduce the cost of microstructure design by joining combinatorial experimentation with generative deep learning models to extract synthesis-composition-microstructure relations. A generative machine learning approach using a conditional generative adversarial network predicts structure zone diagrams. We demonstrate that generative models provide a so far unseen level of quality of generated structure zone diagrams that can be applied for the optimization of chemical composition and processing parameters to achieve a desired microstructure.

薄膜在现代技术中无处不在，并且在材料发现和设计中非常有用。为了获得最佳的外部特性，需要在多参数空间中控制其微观结构，这通常需要太多的实验才能绘制出来。在这里，我们建议掌握薄膜处理的微观结构复杂性，并通过将组合实验与生成型深度学习模型结合以提取合成-成分-微观结构关系，从而降低微观结构设计的成本。使用条件生成对抗网络的生成机器学习方法可预测结构区域图。