Florian Häse
Florian Häse
Bayer AG
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Cited by
Cited by
Self-referencing embedded strings (SELFIES): A 100% robust molecular string representation
M Krenn, F Häse, AK Nigam, P Friederich, A Aspuru-Guzik
Machine Learning: Science and Technology 1 (4), 045024, 2020
Self-driving laboratory for accelerated discovery of thin-film materials
BP MacLeod, FGL Parlane, TD Morrissey, F Häse, LM Roch, ...
Science Advances 6 (20), eaaz8867, 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
Phoenics: a Bayesian optimizer for chemistry
F Hase, LM Roch, C Kreisbeck, A Aspuru-Guzik
ACS central science 4 (9), 1134-1145, 2018
Next-generation experimentation with self-driving laboratories
F Häse, LM Roch, A Aspuru-Guzik
Trends in Chemistry 1 (3), 282-291, 2019
Beyond Ternary OPV: High‐Throughput Experimentation and Self‐Driving Laboratories Optimize Multicomponent Systems
S Langner, F Häse, JD Perea, T Stubhan, J Hauch, LM Roch, ...
Advanced Materials 32 (14), 1907801, 2020
On scientific understanding with artificial intelligence
M Krenn, R Pollice, SY Guo, M Aldeghi, A Cervera-Lierta, P Friederich, ...
Nature Reviews Physics 4 (12), 761-769, 2022
Data-science driven autonomous process optimization
M Christensen, LPE Yunker, F Adedeji, F Häse, LM Roch, T Gensch, ...
Communications Chemistry 4 (1), 112, 2021
ChemOS: orchestrating autonomous experimentation
LM Roch, F Häse, C Kreisbeck, T Tamayo-Mendoza, LPE Yunker, ...
Science Robotics 3 (19), eaat5559, 2018
Machine learning exciton dynamics
F Häse, S Valleau, E Pyzer-Knapp, A Aspuru-Guzik
Chemical Science 7 (8), 5139-5147, 2016
Machine learning directed drug formulation development
P Bannigan, M Aldeghi, Z Bao, F Häse, A Aspuru-Guzik, C Allen
Advanced Drug Delivery Reviews 175, 113806, 2021
ChemOS: An orchestration software to democratize autonomous discovery
LM Roch, F Häse, A Aspuru-Guzik
Chimera: enabling hierarchy based multi-objective optimization for self-driving laboratories
F Häse, LM Roch, A Aspuru-Guzik
Chemical science 9 (39), 7642-7655, 2018
Gryffin: An algorithm for Bayesian optimization of categorical variables informed by expert knowledge
F Häse, M Aldeghi, RJ Hickman, LM Roch, A Aspuru-Guzik
Applied Physics Reviews 8 (3), 2021
How machine learning can assist the interpretation of ab initio molecular dynamics simulations and conceptual understanding of chemistry
F Häse, IF Galván, A Aspuru-Guzik, R Lindh, M Vacher
Chemical science 10 (8), 2298-2307, 2019
Machine learning for quantum dynamics: deep learning of excitation energy transfer properties
F Häse, C Kreisbeck, A Aspuru-Guzik
Chemical science 8 (12), 8419-8426, 2017
Olympus: a benchmarking framework for noisy optimization and experiment planning
F Häse, M Aldeghi, RJ Hickman, LM Roch, M Christensen, E Liles, ...
Machine Learning: Science and Technology 2 (3), 035021, 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
Free energy analysis and mechanism of base pair stacking in nicked DNA
F Häse, M Zacharias
Nucleic acids research 44 (15), 7100-7108, 2016
Machine learning models to accelerate the design of polymeric long-acting injectables
P Bannigan, Z Bao, RJ Hickman, M Aldeghi, F Häse, A Aspuru-Guzik, ...
Nature communications 14 (1), 35, 2023
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