default search action
O. Anatole von Lilienfeld
Person information
- affiliation: University of Toronto, Canada
- affiliation: National Center for Computational Design and Discovery of Novel Materials (MARVEL), Basel, Switzerland
- affiliation: University of Basel, Department of Chemistry, Switzerland
- affiliation: Argonne National Laboratory, Lemont, IL, USA
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
2020 – today
- 2024
- [i2]Vanni Doffini, O. Anatole von Lilienfeld, Michael A. Nash:
Data-Error Scaling in Machine Learning on Natural Discrete Combinatorial Mutation-prone Sets: Case Studies on Peptides and Small Molecules. CoRR abs/2405.05167 (2024) - 2023
- [j10]Jan Weinreich
, Guido Falk von Rudorff, O. Anatole von Lilienfeld
Encrypted machine learning of molecular quantum properties. Mach. Learn. Sci. Technol. 4(2): 25017 (2023) - [j9]Dominik Lemm
Improved decision making with similarity based machine learning: applications in chemistry. Mach. Learn. Sci. Technol. 4(4): 45043 (2023) - 2022
- [i1]Jan Weinreich, Guido Falk von Rudorff, O. Anatole von Lilienfeld:
Encrypted machine learning of molecular quantum properties. CoRR abs/2212.04322 (2022) - 2021
- [j8]Behnam Parsaeifard
An assessment of the structural resolution of various fingerprints commonly used in machine learning. Mach. Learn. Sci. Technol. 2(1): 15018 (2021) - 2020
- [j7]O. Anatole von Lilienfeld
Introducing Machine Learning: Science and Technology. Mach. Learn. Sci. Technol. 1(1): 10201 (2020) - [j6]Stefan N. Heinen
Machine learning the computational cost of quantum chemistry. Mach. Learn. Sci. Technol. 1(2): 25002 (2020) - [j5]Julia Westermayr
Neural networks and kernel ridge regression for excited states dynamics of CH2NH$_2^+$: From single-state to multi-state representations and multi-property machine learning models. Mach. Learn. Sci. Technol. 1(2): 25009 (2020) - [j4]Onur Çaylak, O. Anatole von Lilienfeld, Björn Baumeier
Wasserstein metric for improved quantum machine learning with adjacency matrix representations. Mach. Learn. Sci. Technol. 1(3): 03 (2020) - [j3]Anders S. Christensen
On the role of gradients for machine learning of molecular energies and forces. Mach. Learn. Sci. Technol. 1(4): 45018 (2020) - [j2]Guido Falk von Rudorff
Thousands of reactants and transition states for competing E2 and S$_\mathrm{N}$2 reactions. Mach. Learn. Sci. Technol. 1(4): 45026 (2020)
2010 – 2019
- 2012
- [j1]Matthias Rupp, Alexandre Tkatchenko, Klaus-Robert Müller
Modeling of molecular atomization energies using machine learning. J. Cheminformatics 4(S-1): 33 (2012) - [c1]Grégoire Montavon, Katja Hansen, Siamac Fazli, Matthias Rupp, Franziska Biegler, Andreas Ziehe, Alexandre Tkatchenko, O. Anatole von Lilienfeld, Klaus-Robert Müller:
Learning Invariant Representations of Molecules for Atomization Energy Prediction. NIPS 2012: 449-457
Coauthor Index
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from ,
, and
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and
to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from .
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-06-25 20:59 CEST by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by: