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Stefan Heinen
Stefan Heinen
Postdoc, Vector Institute
Verified email at vectorinstitute.ai
Title
Cited by
Cited by
Year
Machine learning meets volcano plots: computational discovery of cross-coupling catalysts
B Meyer, B Sawatlon, S Heinen, OA Von Lilienfeld, C Corminboeuf
Chemical science 9 (35), 7069-7077, 2018
1932018
Thousands of reactants and transition states for competing E2 and S2 reactions
GF von Rudorff, SN Heinen, M Bragato, OA von Lilienfeld
Machine Learning: Science and Technology 1 (4), 045026, 2020
522020
Toward the design of chemical reactions: Machine learning barriers of competing mechanisms in reactant space
S Heinen, GF von Rudorff, OA von Lilienfeld
The Journal of Chemical Physics 155 (6), 2021
492021
Machine learning the computational cost of quantum chemistry
S Heinen, M Schwilk, GF von Rudorff, OA von Lilienfeld
Machine Learning: Science and Technology 1 (2), 025002, 2020
382020
Transition state search and geometry relaxation throughout chemical compound space with quantum machine learning
S Heinen, GF von Rudorff, OA von Lilienfeld
The Journal of Chemical Physics 157 (22), 2022
82022
Kernel based quantum machine learning at record rate: Many-body distribution functionals as compact representations
D Khan, S Heinen, OA von Lilienfeld
The Journal of Chemical Physics 159 (3), 2023
62023
The quantum chemical search for novel materials and the issue of data processing: The InfoMol project
HP Lüthi, S Heinen, G Schneider, A Glöss, MP Brändle, RA King, ...
Journal of Computational Science 15, 65-73, 2016
42016
Reducing training data needs with minimal multilevel machine learning (M3L)
S Heinen, D Khan, GF von Rudorff, K Karandashev, DJA Arrieta, ...
arXiv preprint arXiv:2308.11196, 2023
32023
Evolutionary Monte Carlo of QM properties in chemical space: Electrolyte design
K Karandashev, J Weinreich, S Heinen, DJ Arismendi Arrieta, ...
Journal of Chemical Theory and Computation 19 (23), 8861-8870, 2023
12023
Quantum Machine Learning Applied to Chemical Reaction Space
S Heinen
University_of_Basel, 2021
12021
Autonomous data extraction from peer reviewed literature for training machine learning models of oxidation potentials
S Lee, S Heinen, D Khan, OA von Lilienfeld
Machine Learning: Science and Technology, 2023
2023
Geometry Relaxation and Transition State Search throughout Chemical Compound Space with Quantum Machine Learning
S Heinen, GF von Rudorff, OA von Lilienfeld
arXiv preprint arXiv:2205.02623, 2022
2022
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