Development of a machine learning-based interatomic potential for silicon carbide (SiC) using atomic cluster expansion (ACE)
Research output: Contribution to conference › Poster › Research › peer-review
Standard
Development of a machine learning-based interatomic potential for silicon carbide (SiC) using atomic cluster expansion (ACE). / Halkali, Celine; Holub, Georg; Rajabzadeh, Zahra et al.
2024. Poster session presented at Data-driven discovery in the chemical sciences Faraday Discussion, Oxford, United Kingdom.
2024. Poster session presented at Data-driven discovery in the chemical sciences Faraday Discussion, Oxford, United Kingdom.
Research output: Contribution to conference › Poster › Research › peer-review
Harvard
Halkali, C, Holub, G, Rajabzadeh, Z & Romaner, L 2024, 'Development of a machine learning-based interatomic potential for silicon carbide (SiC) using atomic cluster expansion (ACE)', Data-driven discovery in the chemical sciences Faraday Discussion, Oxford, United Kingdom, 10/09/24 - 12/09/24.
APA
Halkali, C., Holub, G., Rajabzadeh, Z., & Romaner, L. (2024). Development of a machine learning-based interatomic potential for silicon carbide (SiC) using atomic cluster expansion (ACE). Poster session presented at Data-driven discovery in the chemical sciences Faraday Discussion, Oxford, United Kingdom.
Vancouver
Halkali C, Holub G, Rajabzadeh Z, Romaner L. Development of a machine learning-based interatomic potential for silicon carbide (SiC) using atomic cluster expansion (ACE). 2024. Poster session presented at Data-driven discovery in the chemical sciences Faraday Discussion, Oxford, United Kingdom.
Author
Bibtex - Download
@conference{be694febed9740b993b086136ddffab3,
title = "Development of a machine learning-based interatomic potential for silicon carbide (SiC) using atomic cluster expansion (ACE)",
author = "Celine Halkali and Georg Holub and Zahra Rajabzadeh and Lorenz Romaner",
year = "2024",
language = "English",
note = "Data-driven discovery in the chemical sciences Faraday Discussion ; Conference date: 10-09-2024 Through 12-09-2024",
}
RIS (suitable for import to EndNote) - Download
TY - CONF
T1 - Development of a machine learning-based interatomic potential for silicon carbide (SiC) using atomic cluster expansion (ACE)
AU - Halkali, Celine
AU - Holub, Georg
AU - Rajabzadeh, Zahra
AU - Romaner, Lorenz
PY - 2024
Y1 - 2024
M3 - Poster
T2 - Data-driven discovery in the chemical sciences Faraday Discussion
Y2 - 10 September 2024 through 12 September 2024
ER -
