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Stefan Heinen
Stefan Heinen
Postdoc, Empa, High Performance Ceramics Lab
Подтвержден адрес электронной почты в домене empa.ch
Название
Процитировано
Процитировано
Год
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
2172018
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
632020
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
582021
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
462020
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
182023
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
152022
Reducing training data needs with minimal multilevel machine learning (M3L)
S Heinen, D Khan, GF von Rudorff, K Karandashev, DJA Arrieta, ...
Machine Learning: Science and Technology 5 (2), 025058, 2024
102024
Crash testing machine learning force fields for molecules, materials, and interfaces: molecular dynamics in the TEA challenge 2023
I Poltavsky, M Puleva, A Charkin-Gorbulin, G Fonseca, I Batatia, ...
Chemical Science, 2025
62025
OA vonLilienfeld, C. Corminboeuf
B Meyer, B Sawatlon, S Heinen
Chem. Sci 9, 7069-7077, 2018
52018
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
52016
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 5 (1), 015052, 2024
42024
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
22023
Combining Hammett σ constants for Δ-machine learning and catalyst discovery
VD Rakotonirina, M Bragato, S Heinen, OA von Lilienfeld
Digital Discovery 3 (12), 2487-2496, 2024
12024
Quantum Machine Learning Applied to Chemical Reaction Space
S Heinen
University_of_Basel, 2021
12021
Optimizing woodcutting with zirconia-toughened alumina: Processing, performance, and industrial insights
T Thakur, S Heinen, B Ehrle, G Blugan
Heliyon 11 (2), 2025
2025
Combination rule for Hammett constants in computational catalyst discovery
VD Rakotonirina, M Bragato, S Heinen, OA von Lilienfeld
arXiv preprint arXiv:2405.07747, 2024
2024
Revolutionizing Wood Cutting: Zirconia Toughened Alumina-Sro Advancements
SN Heinen, T Thakur, B Ehrle, G Blugan
Available at SSRN 4895387, 0
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