| Original language | English |
|---|---|
| Pages (from-to) | 8306-8316 |
| Number of pages | 11 |
| Journal | Materials Advances |
| Volume | 3 |
| Issue number | 22 |
| Early online date | 20 Sept 2022 |
| DOIs | |
| Publication status | Published - 21 Nov 2022 |
Funding
We acknowledge support from the European Research Council (ERC-CoG grant QML and H2020 projects BIG-MAP). This project has received funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreements #957189. This project has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement no. 772834). This result only reflects the author's view and the EU is not responsible for any use that may be made of the information it contains. This work was partly supported by the NCCR MARVEL, funded by the Swiss National Science Foundation. Some of the computational results presented have been achieved using the Vienna Scientific Cluster (VSC).
Austrian Fields of Science 2012
- 102019 Machine learning
- 103006 Chemical physics
Keywords
- MOLECULAR-PROPERTIES
- QUANTUM-CHEMISTRY
- MODELS
- SMILES
- PREDICTIONS
- NETWORKS
- SYSTEM
- KERNEL
Fingerprint
Dive into the research topics of 'Selected machine learning of HOMO-LUMO gaps with improved data-efficiency'. Together they form a unique fingerprint.Projects
- 2 Finished
-
QML: Quantum Machine Learning: Chemical Reactions with Unprecedented Speed and Accuracy
von Lilienfeld-Toal, O. A. (Project Lead)
1/10/20 → 31/03/22
Project: Research funding
-
BIG-MAP: Battery Interface Genome - Materials Acceleration Platform
von Lilienfeld-Toal, O. A. (Project Lead)
1/09/20 → 31/08/23
Project: Research funding
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