Quantum Machine Learning: Chemical Reactions with Unprecedented Speed and Accuracy

Project Information

QML

Grant agreement ID: 772834

DOI

10.3030/772834

Project closed

EC signature date 24 January 2018

Start date 1 June 2018

End date 31 May 2024

Funded under

• EXCELLENT SCIENCE - European Research Council (ERC)

Total cost

€ 1 980 500,00

EU contribution

€ 1 980 500,00

Coordinated by

TECHNISCHE UNIVERSITAT BERLIN
Germany

CORDIS provides links to public deliverables and publications of HORIZON projects.

Links to deliverables and publications from FP7 projects, as well as links to some specific result types such as dataset and software, are dynamically retrieved from OpenAIRE .

Publications

Other (7)

Machine Learning of Free Energies in Chemical Compound Space Using Ensemble Representations: Reaching Experimental Uncertainty for Solvation

Author(s): Jan Weinreich, Nicholas J. Browning, O. Anatole von Lilienfeld
Published in: J. Chem. Phys., Issue 154, 2021, Page(s) 134113
Publisher: arXiv
DOI: 10.1063/5.0041548

Large yet bounded: Spin gap ranges in carbenes

Author(s): Max Schwilk, Diana N. Tahchieva, O. Anatole von Lilienfeld
Published in: preprint, 2020
Publisher: arXiv

Quantum based machine learning of competing chemical reaction profiles

Author(s): Stefan Heinen, Guido Falk von Rudorff, O. Anatole von Lilienfeld
Published in: preprint, 2020
Publisher: arXiv

Machine learning the computational cost of quantum chemistry

Author(s): Stefan Heinen, Max Schwilk, Guido Falk von Rudorff, O. Anatole von Lilienfeld
Published in: Mach. Learn.: Sci. Tech. 1 025002 (2020), 2019
Publisher: arxiv
DOI: 10.1088/2632-2153/ab6ac4

Occam's razor for AI: Coarse-graining Hammett Inspired Product Ansatz in Chemical Space

Author(s): Bragato, Marco; von Rudorff, Guido Falk; von Lilienfeld, O. Anatole
Published in: preprint, Issue 5, 2023
Publisher: arxiv
DOI: 10.48550/arxiv.2305.07010

Generalized Alchemical Integral Transform and the multi-electron atom energy

Author(s): Krug, Simon León; von Lilienfeld, O. Anatole
Published in: Issue 1, 2023
Publisher: arxiv

Simplifying inverse material design problems for fixed lattices with alchemical chirality

Author(s): Guido Falk von Rudorff, O. Anatole von Lilienfeld
Published in: Science Advances, 2020
Publisher: arXiv
DOI: 10.1126/sciadv.abf1173

Peer reviewed articles (17)

Geometry Relaxation and Transition State Search throughout Chemical Compound Space with Quantum Machine Learning

Author(s): Heinen, S.; von Rudorff, G. F.; von Lilienfeld, O. A.
Published in: J Chem Phys, Issue 20, 2022, ISSN 1089-7690
Publisher: AIP
DOI: 10.48550/arxiv.2205.02623

Fast and accurate excited states predictions: Machine learning and diabatization

Author(s): Štěpán Sršeň, O. Anatole von Lilienfeld, Petr Slavicek
Published in: Physical Chemistry Chemical Physics, 2023, ISSN 1463-9076
Publisher: Royal Society of Chemistry
DOI: 10.26434/chemrxiv-2023-9pxq1

Impact of noise on inverse design: the case of NMR spectra matching

Author(s): Dominik Lemm, Guido Falk von Rudorff, O. Anatole von Lilienfeld
Published in: Digital Discovery, Issue 3, 2024, Page(s) 136-144, ISSN 2635-098X
Publisher: RSC
DOI: 10.1039/d3dd00132f

Selected machine learning of HOMO–LUMO gaps with improved data-efficiency

Author(s): Bernard Mazouin, Alexandre Alain Schöpfer, O. Anatole von Lilienfeld
Published in: Materials Advances, Issue 3, 2024, Page(s) 8306-8316, ISSN 2633-5409
Publisher: RSC
DOI: 10.1039/d2ma00742h

Data enhanced Hammett-equation: reaction barriers in chemical space

Author(s): Marco Bragato, Guido Falk von Rudorff, O. Anatole von Lilienfeld
Published in: Chemical Science, Issue 11/43, 2020, Page(s) 11859-11868, ISSN 2041-6520
Publisher: Royal Society of Chemistry
DOI: 10.1039/d0sc04235h

Conformer-specific polar cycloaddition of dibromobutadiene with trapped propene ions

Author(s): Ardita Kilaj, Jia Wang, Patrik Straňák, Max Schwilk, Uxía Rivero, Lei Xu, O. Anatole von Lilienfeld, Jochen Küpper, Stefan Willitsch
Published in: Nature Communications, Issue 12, 2022, ISSN 2041-1723
Publisher: Nature Publishing Group
DOI: 10.1038/s41467-021-26309-5

Noncovalent Quantum Machine Learning Corrections to Density Functionals

Author(s): Pál D. Mezei, O. Anatole von Lilienfeld
Published in: Journal of Chemical Theory and Computation, Issue 16/4, 2020, Page(s) 2647-2653, ISSN 1549-9618
Publisher: American Chemical Society
DOI: 10.1021/acs.jctc.0c00181

Toward the design of chemical reactions: Machine learning barriers of competing mechanisms in reactant space

Author(s): Stefan Heinen, Guido Falk von Rudorff, O. Anatole von Lilienfeld
Published in: The Journal of Chemical Physics, Issue 155, 2023, ISSN 0021-9606
Publisher: American Institute of Physics
DOI: 10.1063/5.0059742

Transition state search and geometry relaxation throughout chemical compound space with quantum machine learning

Author(s): Stefan Heinen, Guido Falk von Rudorff, O. Anatole von Lilienfeld
Published in: The Journal of Chemical Physics, Issue 157, 2023, ISSN 0021-9606
Publisher: American Institute of Physics
DOI: 10.1063/5.0112856

Non-covalent interactions between molecular dimers (S66) in electric fields

Author(s): Max Schwilk, Pál D Mezei, Diana N Tahchieva, O Anatole von Lilienfeld
Published in: Electronic Structure, Issue 4, 2022, Page(s) 014005, ISSN 2516-1075
Publisher: IOPP
DOI: 10.1088/2516-1075/ac4eeb

Evolutionary Monte Carlo of QM Properties in Chemical Space: Electrolyte Design

Author(s): Konstantin Karandashev, Jan Weinreich, Stefan Heinen, Daniel Jose Arismendi Arrieta, Guido Falk von Rudorff, Kersti Hermansson, O. Anatole von Lilienfeld
Published in: Journal of Chemical Theory and Computation, Issue 19, 2023, Page(s) 8861-8870, ISSN 1549-9618
Publisher: American Chemical Society
DOI: 10.1021/acs.jctc.3c00822

Kernel based quantum machine learning at record rate: Many-body distribution functionals as compact representations

Author(s): Danish Khan, Stefan Heinen, O. Anatole von Lilienfeld
Published in: The Journal of Chemical Physics, Issue 159, 2023, ISSN 0021-9606
Publisher: American Institute of Physics
DOI: 10.1063/5.0152215

Towards self-driving laboratories: The central role of density functional theory in the AI age

Author(s): Huang, Bing; von Rudorff, Guido Falk; von Lilienfeld, O. Anatole
Published in: Science, 2023, ISSN 1095-9203
Publisher: AAAS
DOI: 10.48550/arxiv.2304.03272

<i>Ab initio</i> machine learning of phase space averages

Author(s): Jan Weinreich, Dominik Lemm, Guido Falk von Rudorff, O. Anatole von Lilienfeld
Published in: The Journal of Chemical Physics, Issue 157, 2023, ISSN 0021-9606
Publisher: American Institute of Physics
DOI: 10.1063/5.0095674

Reducing training data needs with minimal multilevel machine learning (M3L)

Author(s): Stefan Heinen, Danish Khan, Guido Falk von Rudorff, Konstantin Karandashev, Daniel Jose Arismendi Arrieta, Alastair J A Price, Surajit Nandi, Arghya Bhowmik, Kersti Hermansson, O Anatole von Lilienfeld
Published in: Machine Learning: Science and Technology, Issue 5, 2024, Page(s) 025058, ISSN 2632-2153
Publisher: IOPP
DOI: 10.1088/2632-2153/ad4ae5

Encrypted machine learning of molecular quantum properties

Author(s): Jan Weinreich, Guido Falk von Rudorff, O Anatole von Lilienfeld
Published in: Machine Learning: Science and Technology, Issue 4, 2023, Page(s) 025017, ISSN 2632-2153
Publisher: IOPP
DOI: 10.1088/2632-2153/acc928

Relative energies without electronic perturbations via alchemical integral transform

Author(s): Simon León Krug, Guido Falk von Rudorff, O. Anatole von Lilienfeld
Published in: The Journal of Chemical Physics, Issue 157, 2023, ISSN 0021-9606
Publisher: American Institute of Physics
DOI: 10.1063/5.0111511