Tailoring Complexity for Catalyst Discovery Using Physically Motivated Machine Learning
- Weitere Titel
-
Entdeckung neuartiger Katalysatormaterialien durch physikalisch motiviertes maschinelles Lernen maßgeschneideter Komplexität
- Standort
-
Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
-
Online-Ressource
- Sprache
-
Englisch
- Anmerkungen
-
München, Technische Universität München, Dissertation, 2022
- Schlagwort
-
Heterogene Katalyse
Katalyse
Computational chemistry
Katalysator
Dichtefunktionalformalismus
Reaktionskinetik
- Ereignis
-
Veröffentlichung
- (wo)
-
München
- (wer)
-
Universitätsbibliothek der TU München
- (wann)
-
2022
- Urheber
-
Xu, Wenbin
- Beteiligte Personen und Organisationen
- URN
-
urn:nbn:de:bvb:91-diss-20221125-1689045-1-4
- Rechteinformation
-
Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
-
15.08.2025, 07:26 MESZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Beteiligte
- Xu, Wenbin
- Reuter, Karsten Ulrich
- Hofmann, Oliver T.
- Heiz, Ulrich K.
- Universitätsbibliothek der TU München
Entstanden
- 2022
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