AtLv-AIGaN

Atomic-level control of AlGaN hetero-interfaces for deep-UV LED

© Marie-Paule PILENI/Nicolas GOUBET/ERC/CNRS Images
  • Yoshihiro Kangawa - Kyushu University - Japan
  • Vesselin Tonchev - Sofia University - Bulgaria
  • Hristina Popova - Institute of Physical Chemistry of the Bulgarian Academy of Sciences - Bulgaria
  • Paweł Kempisty - Institute of High Pressure Physics of the Polish Academy of Sciences - Poland
  • Magdalena Załuska-Kotur - Institute of Physics of the Polish Academy of Sciences - Poland
  • Hideto Miyake - Mie University - Japan

In conventional new materials development, crystal growth conditions are optimized by trial and error. In recent years, on the other hand, there is a need to innovate and apply process informatics (PI), the development of new materials using artificial intelligence (AI).

In this research project, a Digital Twin based on firstprinciples calculations and cellular automata will be developed to establish a framework for exploring materials processes through machine learning. This innovative PI technology will be used to develop deep-ultraviolet (DUV) LEDs that contribute to the destruction and inactivation of RNA and DNA of COVID-19 viruses and bacteria.

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