BIGDML - Towards accurate quantum machine learning force fields for materials
- Standort
-
Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
-
Online-Ressource
- Sprache
-
Englisch
- Erschienen in
-
In: Nature Communications (13:) - Heidelberg : Springer Nature - Art.-Id. 3733
- Ereignis
-
Veröffentlichung
- (wo)
-
Berlin
- (wer)
-
Technische Universität Berlin
- (wann)
-
2022
- Urheber
-
Sauceda, Huziel E.
Müller, Klaus-Robert
Tkatchenko, Alexandre
Gálvez-González, Luis E.
Chmiela, Stefan
Paz-Borbón, Lauro Oliver
- DOI
-
10.14279/depositonce-15968
- Handle
-
11303/17189
- URN
-
urn:nbn:de:101:1-2022071302070186980246
- 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
- Sauceda, Huziel E.
- Müller, Klaus-Robert
- Tkatchenko, Alexandre
- Gálvez-González, Luis E.
- Chmiela, Stefan
- Paz-Borbón, Lauro Oliver
- Technische Universität Berlin
Entstanden
- 2022
Ähnliche Objekte (12)
Machine learning of accurate energy-conserving molecular force fields
Novel machine learning methods for computational chemistry
Spärlich verbundene neuronale Netze und ihre Anwendung
Bag of Machine Learning Concepts for Visual Concept Recognition in Images
Towards exact molecular dynamics simulations with machine-learned force fields
Massive parallelization of combinatorial statistical genetics analyses porting machine learning methods on general purpose graphics processing units (GPU)
A Euclidean transformer for fast and stable machine learned force fields
Towards more efficient and performant computations in quantum chemistry with machine learning
Neuronal correlates of emotions in human-machine interaction
Machine learning of solvent effects on molecular spectra and reactions
Machine learning force fields
Probabilistic Methods for Network Security. From Analysis to Response
Machine learning of accurate energy-conserving molecular force fields
Novel machine learning methods for computational chemistry
Spärlich verbundene neuronale Netze und ihre Anwendung
Bag of Machine Learning Concepts for Visual Concept Recognition in Images
Towards exact molecular dynamics simulations with machine-learned force fields
Massive parallelization of combinatorial statistical genetics analyses porting machine learning methods on general purpose graphics processing units (GPU)
A Euclidean transformer for fast and stable machine learned force fields
Towards more efficient and performant computations in quantum chemistry with machine learning
Neuronal correlates of emotions in human-machine interaction
Machine learning of solvent effects on molecular spectra and reactions
Machine learning force fields
Probabilistic Methods for Network Security. From Analysis to Response
Machine learning of accurate energy-conserving molecular force fields
Novel machine learning methods for computational chemistry
Spärlich verbundene neuronale Netze und ihre Anwendung
Bag of Machine Learning Concepts for Visual Concept Recognition in Images
Towards exact molecular dynamics simulations with machine-learned force fields
Massive parallelization of combinatorial statistical genetics analyses porting machine learning methods on general purpose graphics processing units (GPU)
A Euclidean transformer for fast and stable machine learned force fields
Towards more efficient and performant computations in quantum chemistry with machine learning
Neuronal correlates of emotions in human-machine interaction
Machine learning of solvent effects on molecular spectra and reactions
Machine learning force fields