Determining, understanding, and predicting the so-called structure-property relation is an important task in many scientific disciplines, such as chemistry, biology, meteorology, physics, engineering, and materials science. Structure refers to the spatial distribution of, e.g., substances, material, or matter in general, while property is a resulting characteristic that usually depends in a non-trivial way on spatial details of the structure. Traditionally, forward simulations models have been used for such tasks. Recently, several machine learning algorithms have been applied in these scientific fields to enhance and accelerate simulation models or as surrogate models. In this work, we develop and investigate the applications of six machine learning techniques based on two different datasets from the domain of materials science: data from a two-dimensional Ising model for predicting the formation of magnetic domains and data representing the evolution of dual-phase microstructures from the Cahn-Hilliard model. We analyze the accuracy and robustness of all models and elucidate the reasons for the differences in their performances. The impact of including domain knowledge through tailored features is studied, and general recommendations based on the availability and quality of training data are derived from this.

翻译：确定、理解并预测所谓的结构-属性关系是许多科学学科（如化学、生物学、气象学、物理学、工程学以及材料科学）中的重要任务。结构指物质、材料或一般物质在空间上的分布，而属性则是通常以非平凡方式依赖于结构空间细节的结果特征。传统上，正向模拟模型被用于此类任务。近年来，多种机器学习算法被应用于这些科学领域，以增强和加速模拟模型，或作为替代模型。在本研究中，我们基于材料科学领域的两组不同数据集，开发并研究了六种机器学习技术的应用：一组来自用于预测磁畴形成的二维伊辛模型数据，另一组代表来自Cahn-Hilliard模型的微观结构演化数据。我们分析了所有模型的准确性和鲁棒性，并阐明了其性能差异的原因。研究了通过定制特征纳入领域知识的影响，并根据训练数据的可用性和质量得出了通用性建议。