Joint Workshop LANL/NIMS Quantum and Functional Materials and MANA International Symposium 2024


Quantum Materials - 17

Title

Local structure analysis of disorders in GaN using machine-learning and DFT methods

Author's photo

Authors

Amran Mahfudh Yatmeidhy, Timothee Jamin, Anh Khoa Augustin Lu
and Tsuyoshi Miyazaki

Affiliations

Quantum Materials Simulation Group, MANA, NIMS

URL

https://www.nims.go.jp/cmsc/fps1/index.html

Email

yatmeidhy.amran@nims.go.jp

Abstract

Understanding the structural disorders such as phase transitions, atom bombardment, and defects in GaN is of importance aspect in the development of high-quality GaN-based devices. Such process is difficult to observe experimentally due to taking place at extreme pressure and temperature conditions and/or requires such a sophisticated technique as well as involves local structural modification. Capturing this process should be valuable for the characterization of GaN at critical conditions such as a melting point which has been an unresolved problem in the study of GaN [1]. Utilizing unsupervised learning method combining the two-step locality preserving projections (TS-LPP) dimensionality reduction technique with locally averaged atomic fingerprints (LAAF) descriptor based on atom-centered symmetry functions (ACSF) [2,3], we analyze the structural disorders in GaN. Our preliminary results show a proper distinguished local structure of crystalline and liquid states around the phase transition temperature of GaN (see Fig. 1). It should be noted that the high melting temperature in the present results comes from the small simulation cell of perfect crystalline and short simulation time.

Fig. 1. Distribution of data points after dimension reduction by TS-LPP for crystalline (300 K, 1000 K, 2000 K, 3000K, 3500 K, 4000 K) and liquid states (4500 K, 5000 K) of GaN.

Reference

  1. S. Porowski, B. Sadovyi, S. Gierlotka, S.J. Rzoska, I. Grzegory, I. Petrusha, V. Turkevich, and D. Stratiichuk, J. Phys. Chem. Solids., 85, 138-143, (2015). DOI 10.1016/j.jpcs.2015.05.006
  2. R. Tamura, M. Matsuda, J. Lin, Y. Futamura, T. Sakurai, and T. Miyazaki, Phys. Rev. B 105, 075107, (2022). DOI 10.1103/PhysRevB.105.075107
  3. A. K. A. Lu, J. Lin, Y. Futamura, T. Sakurai, R. Tamura, and T. Miyazaki, Phys. Chem. Chem. Phys. 26, 11657-11666, (2024). DOI 10.1039/d3cp06298h