Computational Materials Science2022,Vol.21110.DOI:10.1016/j.commatsci.2022.111503

Recent progress on the mesoscale modeling of architected thin-films via phase-field formulations of physical vapor deposition

Stewart, James A.
Computational Materials Science2022,Vol.21110.DOI:10.1016/j.commatsci.2022.111503

Recent progress on the mesoscale modeling of architected thin-films via phase-field formulations of physical vapor deposition

Stewart, James A.1
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作者信息

  • 1. Sandia Natl Labs
  • 折叠

Abstract

Thin-film coatings can be found everywhere in modern technological applications due to desirable electrical, mechanical, chemical, and optical properties. These properties directly depend upon the thin-film's microstruc-tural features, which are themselves influenced by the materials and vapor-deposition processing conditions used for fabrication. As such, understanding processing-microstructure relationships is essential to designing thin-films with optimized properties, and discovering new processing conditions that allow for novel thin-films with multifunctional microstructures. Here, a short review is presented on recent developments that utilize the phase-field method to simultaneously model the vapor-deposition process and corresponding microstructure formation at the mesoscale. Phase-field-based vapor-deposition models that simulate thin-film growth of immiscible alloy and polycrystalline systems are highlighted in addition to machine-learning-based surrogate models that can facilitate accelerated high-fidelity simulations along with materials design and exploration studies.

Key words

Phase-field modeling/Physical vapor deposition/Thin-films/Machine learning/Surrogate models/Materials design/GRAIN-GROWTH/COMPUTER-SIMULATION/MICROSTRUCTURE MORPHOLOGY/MATERIALS INFORMATICS/BENCHMARK PROBLEMS/EVOLUTION/SEPARATION/DYNAMICS/SYSTEM/MECHANISMS

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出版年

2022
Computational Materials Science

Computational Materials Science

EISCI
ISSN:0927-0256
参考文献量78
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