Pascal Friederich
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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
Machine-learned potentials for next-generation matter simulations
P Friederich, F Häse, J Proppe, A Aspuru-Guzik
Nature Materials 20 (6), 750-761, 2021
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
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
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
Augmenting Genetic Algorithms with Deep Neural Networks for Exploring the Chemical Space
AK Nigam, P Friederich, M Krenn, A Aspuru-Guzik
8th International Conference on Learning Representations (ICLR) 2020, 2019
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
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
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
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
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
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
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
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
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
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
Designing and understanding light-harvesting devices with machine learning
F Häse, LM Roch, P Friederich, A Aspuru-Guzik
Nature Communications 11 (1), 4587, 2020
Automatic discovery of photoisomerization mechanisms with nanosecond machine learning photodynamics simulations
J Li, P Reiser, BR Boswell, A Eberhard, NZ Burns, P Friederich, SA Lopez
Chemical Science, 2021
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
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
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