% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@ARTICLE{Nguyen:1025204,
      author       = {Nguyen, Binh Duong and Potapenko, Pavlo and Demirci,
                      Aytekin and Govind, Kishan and Bompas, Sébastien and
                      Sandfeld, Stefan},
      title        = {{E}fficient surrogate models for materials science
                      simulations: {M}achine learning-based prediction of
                      microstructure properties},
      journal      = {Machine learning with applications},
      volume       = {16},
      issn         = {2666-8270},
      address      = {Amsterdam},
      publisher    = {Elsevier},
      reportid     = {FZJ-2024-02772},
      pages        = {100544 -},
      year         = {2024},
      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.},
      cin          = {IAS-9},
      ddc          = {004},
      cid          = {I:(DE-Juel1)IAS-9-20201008},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511)},
      pid          = {G:(DE-HGF)POF4-5111},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:001289088000001},
      doi          = {10.1016/j.mlwa.2024.100544},
      url          = {https://juser.fz-juelich.de/record/1025204},
}