Efficient surrogate models for materials science simulations: Machine learning-based prediction of microstructure properties

Machine Learning with Applications, vol. 16, pp. 100544

Abstract

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.

Authors 6

1. Binh Duong Nguyen

   Forschungszentrum Jülich

   Affiliation as printed

   Institute for Advanced Simulations – Materials Data Science and Informatics (IAS-9), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany

2. Pavlo Potapenko

   Forschungszentrum Jülich

   Affiliation as printed

   Institute for Advanced Simulations – Materials Data Science and Informatics (IAS-9), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany

3. Aytekin Demirci

   Forschungszentrum Jülich

   Affiliation as printed

   Institute for Advanced Simulations – Materials Data Science and Informatics (IAS-9), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany

4. Kishan Govind

   Forschungszentrum Jülich

   Affiliation as printed

   Institute for Advanced Simulations – Materials Data Science and Informatics (IAS-9), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany

5. Sébastien Bompas

   Forschungszentrum Jülich

   Affiliation as printed

   Institute for Advanced Simulations – Materials Data Science and Informatics (IAS-9), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany

6. Stefan Sandfeld corresponding Aachen Chair of Materials Data Science and Materials Informatics Faculty 5 – Georesources and Materials Engineering

   Forschungszentrum Jülich · RWTH Aachen University

   Affiliation as printed

   Chair of Materials Data Science and Materials Informatics, Faculty 5 – Georesources and Materials Engineering, RWTH Aachen University, 52056 Aachen, Germany

   Institute for Advanced Simulations – Materials Data Science and Informatics (IAS-9), Forschungszentrum Jülich GmbH, 52425 Jülich, Germany