F
Florian Häse
Researcher at Harvard University
Publications - 47
Citations - 2692
Florian Häse is an academic researcher from Harvard University. The author has contributed to research in topics: Bayesian optimization & Computer science. The author has an hindex of 19, co-authored 45 publications receiving 1382 citations. Previous affiliations of Florian Häse include Technische Universität München & University of Toronto.
Papers
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Journal ArticleDOI
Self-referencing embedded strings (SELFIES): A 100% robust molecular string representation
Mario Krenn,Florian Häse,AkshatKumar Nigam,Pascal Friederich,Pascal Friederich,Alán Aspuru-Guzik +5 more
TL;DR: SELFIES (SELF-referencIng Embedded Strings), a string-based representation of molecules which is 100\% robust and allows for explanation and interpretation of the internal working of the generative models.
Journal ArticleDOI
Self-driving laboratory for accelerated discovery of thin-film materials.
Benjamin P. MacLeod,Fraser G. L. Parlane,Thomas D. Morrissey,Florian Häse,Loïc M. Roch,Kevan E. Dettelbach,Raphaell Moreira,Lars P. E. Yunker,Michael B. Rooney,Joseph R. Deeth,Veronica Lai,Gordon J. Ng,Henry Situ,Ray H. Zhang,Michael S. Elliott,Ted H. Haley,David J. Dvorak,Alán Aspuru-Guzik,Jason E. Hein,Curtis P. Berlinguette +19 more
TL;DR: In this article, a modular robotic platform driven by a model-based optimization algorithm is used to optimize optical and electronic properties of thin-film materials by modifying the film composition and processing conditions.
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Phoenics: A Bayesian Optimizer for Chemistry.
TL;DR: Phoenics is a probabilistic global optimization algorithm identifying the set of conditions of an experimental or computational procedure which satisfies desired targets, and is recommended for rapid optimization of unknown expensive-to-evaluate objective functions, such as experimentation or long-lasting computations.
Journal ArticleDOI
Next-Generation Experimentation with Self-Driving Laboratories
TL;DR: Self-driving laboratories promise to substantially accelerate the discovery process by augmenting automated experimentation platforms with artificial intelligence (AI), which actively search for promising experimental procedures by hypothesizing about their outcomes based on previous experiments.
Journal ArticleDOI
Beyond Ternary OPV: High-Throughput Experimentation and Self-Driving Laboratories Optimize Multicomponent Systems.
Stefan Langner,Florian Häse,José Darío Perea,Tobias Stubhan,Jens Hauch,Loïc M. Roch,Thomas Heumueller,Alán Aspuru-Guzik,Christoph J. Brabec,Christoph J. Brabec +9 more
TL;DR: The development of high-throughput and autonomous experimentation methods for the effective optimization of multicomponent polymer blends for OPVs and a method for automated film formation enabling the fabrication of up to 6048 films per day is introduced.