Development of a machine learning-based interatomic potential for silicon carbide (SiC) using atomic cluster expansion (ACE)

Publikation: Konferenzbeitrag › Poster › Forschung › Begutachtung

Originalsprache	Englisch
Publikationsstatus	Veröffentlicht - 2024
Veranstaltung	Data-driven discovery in the chemical sciences Faraday Discussion - Oxford, Großbritannien / Vereinigtes Königreich Dauer: 10 Sept. 2024 → 12 Sept. 2024

Sonstiges	Data-driven discovery in the chemical sciences Faraday Discussion
Land/Gebiet	Großbritannien / Vereinigtes Königreich
Ort	Oxford
Zeitraum	10/09/24 → 12/09/24

Dieses zitieren

@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",

}

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. Postersitzung präsentiert bei Data-driven discovery in the chemical sciences Faraday Discussion, Oxford, Großbritannien / Vereinigtes Königreich.

Publikation: Konferenzbeitrag › Poster › Forschung › Begutachtung

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 -