Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Filters

Florian Häse

Showing results (1-10 of 20) with videos related to

Pageof 2
Sort By:
Nucleic Acids Research|July 14, 2016
Free energy analysis and mechanism of base pair stacking in nicked DNAFlorian Häse, Martin Zacharias
Chemical Science|April 6, 2018
Machine learning for quantum dynamics: deep learning of excitation energy transfer propertiesFlorian Häse, Christoph Kreisbeck, Alán Aspuru-Guzik
Chemical Science|November 6, 2018
Chimera: enabling hierarchy based multi-objective optimization for self-driving laboratoriesFlorian Häse, Loïc M Roch, Alán Aspuru-Guzik
Nature Materials|May 28, 2021
Machine-learned potentials for next-generation matter simulationsPascal Friederich, Florian Häse, Jonny Proppe, et al.
Nature Communications|September 12, 2020
Designing and understanding light-harvesting devices with machine learningFlorian Häse, Loïc M Roch, Pascal Friederich, et al.
Chemical Science|August 30, 2018
Machine learning exciton dynamicsFlorian Häse, Stéphanie Valleau, Edward Pyzer-Knapp, et al.
ACS Central Science|October 3, 2018
Phoenics: A Bayesian Optimizer for ChemistryFlorian Häse, Loïc M Roch, Christoph Kreisbeck, et al.
Chemical Science|November 25, 2021
Golem: an algorithm for robust experiment and process optimizationMatteo Aldeghi, Florian Häse, Riley J Hickman, et al.
Chemical Science|March 19, 2019
How machine learning can assist the interpretation of <i>ab initio</i> molecular dynamics simulations and conceptual understanding of chemistryFlorian Häse, Ignacio Fdez Galván, Alán Aspuru-Guzik, et al.
Biophysical Journal|February 5, 2015
A compact native 24-residue supersecondary structure derived from the villin headpiece subdomainHenry G Hocking, Florian Häse, Tobias Madl, et al.
Pageof 2

Showing results (1-10 of 20) with videos related to

Sort By:
Pageof 2
Nucleic Acids Research|July 14, 2016
Free energy analysis and mechanism of base pair stacking in nicked DNAFlorian Häse, Martin Zacharias
Chemical Science|April 6, 2018
Machine learning for quantum dynamics: deep learning of excitation energy transfer propertiesFlorian Häse, Christoph Kreisbeck, Alán Aspuru-Guzik
Chemical Science|November 6, 2018
Chimera: enabling hierarchy based multi-objective optimization for self-driving laboratoriesFlorian Häse, Loïc M Roch, Alán Aspuru-Guzik
Nature Materials|May 28, 2021
Machine-learned potentials for next-generation matter simulationsPascal Friederich, Florian Häse, Jonny Proppe, et al.
Nature Communications|September 12, 2020
Designing and understanding light-harvesting devices with machine learningFlorian Häse, Loïc M Roch, Pascal Friederich, et al.
Chemical Science|August 30, 2018
Machine learning exciton dynamicsFlorian Häse, Stéphanie Valleau, Edward Pyzer-Knapp, et al.
ACS Central Science|October 3, 2018
Phoenics: A Bayesian Optimizer for ChemistryFlorian Häse, Loïc M Roch, Christoph Kreisbeck, et al.
Chemical Science|November 25, 2021
Golem: an algorithm for robust experiment and process optimizationMatteo Aldeghi, Florian Häse, Riley J Hickman, et al.
Chemical Science|March 19, 2019
How machine learning can assist the interpretation of <i>ab initio</i> molecular dynamics simulations and conceptual understanding of chemistryFlorian Häse, Ignacio Fdez Galván, Alán Aspuru-Guzik, et al.
Biophysical Journal|February 5, 2015
A compact native 24-residue supersecondary structure derived from the villin headpiece subdomainHenry G Hocking, Florian Häse, Tobias Madl, et al.
Pageof 2