| Self-Referencing Embedded Strings (SELFIES): A 100% robust molecular string representation M Krenn, F Hase, AK Nigam, P Friederich, A Aspuru-Guzik Machine Learning: Science and Technology, 2020 | 558* | 2020 |
| Machine-learned potentials for next-generation matter simulations P Friederich, F Häse, J Proppe, A Aspuru-Guzik Nature Materials 20 (6), 750-761, 2021 | 230 | 2021 |
| Toward design of novel materials for organic electronics P Friederich, A Fediai, S Kaiser, M Konrad, N Jung, W Wenzel Advanced Materials 31 (26), 1808256, 2019 | 126 | 2019 |
| Ab initio treatment of disorder effects in amorphous organic materials: Toward parameter free materials simulation P Friederich, F Symalla, V Meded, T Neumann, W Wenzel Journal of Chemical Theory and Computation 10 (9), 3720-3725, 2014 | 125 | 2014 |
| Augmenting Genetic Algorithms with Deep Neural Networks for Exploring the Chemical Space AK Nigam, P Friederich, M Krenn, A Aspuru-Guzik arXiv preprint arXiv:1909.11655, 2019 | 124 | 2019 |
| A comprehensive discovery platform for organophosphorus ligands for catalysis T Gensch, G dos Passos Gomes, P Friederich, E Peters, T Gaudin, ... Journal of the American Chemical Society 144 (3), 1205-1217, 2022 | 108 | 2022 |
| Graph neural networks for materials science and chemistry P Reiser, M Neubert, A Eberhard, L Torresi, C Zhou, C Shao, H Metni, ... Communications Materials 3 (1), 93, 2022 | 106 | 2022 |
| Machine learning dihydrogen activation in the chemical space surrounding Vaska's complex P Friederich, G dos Passos Gomes, R De Bin, A Aspuru-Guzik, D Balcells Chemical Science 11 (18), 4584-4601, 2020 | 102 | 2020 |
| Molecular origin of the charge carrier mobility in small molecule organic semiconductors P Friederich, V Meded, A Poschlad, T Neumann, V Rodin, V Stehr, ... Advanced Functional Materials 26 (31), 5757-5763, 2016 | 91 | 2016 |
| On scientific understanding with artificial intelligence M Krenn, R Pollice, SY Guo, M Aldeghi, A Cervera-Lierta, P Friederich, ... Nature Reviews Physics, 1-9, 2022 | 90 | 2022 |
| Host dependence of the electron affinity of molecular dopants J Li, I Duchemin, OM Roscioni, P Friederich, M Anderson, E Da Como, ... Materials Horizons 6 (1), 107-114, 2019 | 84 | 2019 |
| Superexchange Charge Transport in Loaded Metal Organic Frameworks T Neumann, J Liu, T Wächter, P Friederich, F Symalla, A Welle, ... ACS nano 10 (7), 7085-7093, 2016 | 71 | 2016 |
| Built-in potentials induced by molecular order in amorphous organic thin films P Friederich, V Rodin, F von Wrochem, W Wenzel ACS applied materials & interfaces 10 (2), 1881-1887, 2018 | 69 | 2018 |
| MOF Synthesis Prediction Enabled by Automatic Data Mining and Machine Learning Y Luo, S Bag, O Zaremba, A Cierpka, J Andreo, S Wuttke, P Friederich, ... Angewandte Chemie International Edition 61 (19), e202200242, 2022 | 64 | 2022 |
| Organic molecules with inverted gaps between first excited singlet and triplet states and appreciable fluorescence rates R Pollice, P Friederich, C Lavigne, G dos Passos Gomes, A Aspuru-Guzik Matter 4 (5), 1654-1682, 2021 | 64 | 2021 |
| Designing and understanding light-harvesting devices with machine learning F Häse, LM Roch, P Friederich, A Aspuru-Guzik Nature Communications 11 (1), 4587, 2020 | 64 | 2020 |
| Ab initio charge-carrier mobility model for amorphous molecular semiconductors A Massé, P Friederich, F Symalla, F Liu, R Nitsche, R Coehoorn, ... Physical Review B 93 (19), 195209, 2016 | 60 | 2016 |
| SELFIES and the future of molecular string representations M Krenn, Q Ai, S Barthel, N Carson, A Frei, NC Frey, P Friederich, ... Patterns 3 (10), 2022 | 53 | 2022 |
| Neural message passing on high order paths D Flam-Shepherd, TC Wu, P Friederich, A Aspuru-Guzik Machine Learning: Science and Technology 2 (4), 045009, 2021 | 51 | 2021 |
| Charge Transport by Superexchange in Molecular Host-Guest Systems F Symalla, P Friederich, A Massé, V Meded, R Coehoorn, P Bobbert, ... Physical Review Letters 117 (27), 276803, 2016 | 51 | 2016 |
Wolfgang WenzelKarlsruhe Institute of TechnologyVerified email at kit.edu
