Efficient Surrogate Models for Materials Science Simulations: Machine Learning-based Prediction of Microstructure Properties
This work addresses the need for efficient surrogate models in materials science to accelerate simulations, but it is incremental as it applies existing methods to new data with minor enhancements.
The paper tackled predicting microstructure properties in materials science by developing and comparing six machine learning techniques on two datasets, finding that including domain knowledge through tailored features improved accuracy and robustness, with specific performance differences analyzed.
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.