TY  - JOUR
AU  - Nguyen, Binh Duong
AU  - Potapenko, Pavlo
AU  - Demirci, Aytekin
AU  - Govind, Kishan
AU  - Bompas, Sébastien
AU  - Sandfeld, Stefan
TI  - Efficient surrogate models for materials science simulations: Machine learning-based prediction of microstructure properties
JO  - Machine learning with applications
VL  - 16
SN  - 2666-8270
CY  - Amsterdam
PB  - Elsevier
M1  - FZJ-2024-02772
SP  - 100544 -
PY  - 2024
AB  - 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.
LB  - PUB:(DE-HGF)16
UR  - <Go to ISI:>//WOS:001289088000001
DO  - DOI:10.1016/j.mlwa.2024.100544
UR  - https://juser.fz-juelich.de/record/1025204
ER  -