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Project page for "Physics-informed graph neural networks accelerating microneedle simulations towards novelty of micro-nano scale materials discovery" as a part of Romrawin Chumpu's master thesis and publication.

TL;DR - We purpose a novel method combinding between materials simulation and machine learning to accelerate materials discovery of microneedles. Our work trained with physics-informed dataset. The best model result is graph attention networks (GAT).

Dataset Instruction

Code Instruction

Citation

@article{chumpu2023mnmatdis,
  title = {Physics-informed graph neural networks accelerating microneedle simulations towards novelty of micro-nano scale materials discovery},
  journal = {Engineering Applications of Artificial Intelligence},
  volume = {126},
  pages = {106894},
  year = {2023},
  issn = {0952-1976},
  doi = {https://doi.org/10.1016/j.engappai.2023.106894},
  url = {https://www.sciencedirect.com/science/article/pii/S0952197623010783},
  author = {Romrawin Chumpu and Chun-Lin Chu and Tanyakarn Treeratanaphitak and Sanparith Marukatat and Shu-Han Hsu},
  keywords = {Machine learning, Graph neural networks, Microneedle, Numerical simulation},
}