Florian Häse
Florian Häse
Harvard University, Vector Institute for Artificial Intelligence
Verified email at g.harvard.edu - Homepage
Cited by
Cited by
Phoenics: A Bayesian Optimizer for Chemistry
F Häse, LM Roch, C Kreisbeck, A Aspuru-Guzik
ACS Central Science, 2018
Machine learning exciton dynamics
F Häse, S Valleau, E Pyzer-Knapp, A Aspuru-Guzik
Chemical science 7 (8), 5139-5147, 2016
ChemOS: orchestrating autonomous experimentation
LM Roch, F Häse, C Kreisbeck, T Tamayo-Mendoza, LPE Yunker, ...
Science Robotics 3 (19), 2018
Next-generation experimentation with self-driving laboratories
F Häse, LM Roch, A Aspuru-Guzik
Trends in Chemistry 1 (3), 282-291, 2019
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
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
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
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
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
Free energy analysis and mechanism of base pair stacking in nicked DNA
F Häse, M Zacharias
Nucleic acids research 44 (15), 7100-7108, 2016
ChemOS: An orchestration software to democratize autonomous discovery
LM Roch, F Häse, C Kreisbeck, T Tamayo-Mendoza, LPE Yunker, ...
PLoS One 15 (4), e0229862, 2020
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
Absence of selection for quantum coherence in the fenna–matthews–olson complex: A combined evolutionary and excitonic study
S Valleau, RA Studer, F Häse, C Kreisbeck, RG Saer, RE Blankenship, ...
ACS central science 3 (10), 1086-1095, 2017
An Algorithm for Bayesian Optimization for Categorical Variables Informed by Physical Intuition with Applications to Chemistry
F Häse, LM Roch, AG Aspuru-Guzik
arXiv preprint arXiv:2003.12127 [physics, stat], 2020
Designing and understanding light-harvesting devices with machine learning
F Häse, LM Roch, P Friederich, A Aspuru-Guzik
Nature Communications 11 (1), 1-11, 2020
From absorption spectra to charge transfer in nanoaggregates of oligomers with machine learning
LM Roch, SK Saikin, F Hase, P Friederich, RH Goldsmith, S León, ...
ACS nano 14 (6), 6589-6598, 2020
Oscillatory active-site motions correlate with kinetic isotope effects in formate dehydrogenase
P Pagano, Q Guo, C Ranasinghe, E Schroeder, K Robben, F Hase, H Ye, ...
ACS Catalysis 9 (12), 11199-11206, 2019
Team-Based Learning for Scientific Computing and Automated Experimentation: Visualization of Colored Reactions
S Vargas, S Zamirpour, S Menon, A Rothman, F Häse, ...
Journal of Chemical Education 97 (3), 689-694, 2020
A compact native 24-residue supersecondary structure derived from the villin headpiece subdomain
HG Hocking, F Häse, T Madl, M Zacharias, M Rief, G Žoldák
Biophysical journal 108 (3), 678-686, 2015
Olympus: a benchmarking framework for noisy optimization and experiment planning
F Häse, M Aldeghi, RJ Hickman, LM Roch, M Christensen, E Liles, ...
arXiv preprint arXiv:2010.04153, 2020
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