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Kevin Maik Jablonka

Showing results (1-10 of 28) with videos related to

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Nature Chemistry|April 5, 2022
Making the collective knowledge of chemistry open and machine actionableKevin Maik Jablonka, Luc Patiny, Berend Smit
Journal of Chemical Theory and Computation|August 24, 2019
Applicability of Tail Corrections in the Molecular Simulations of Porous MaterialsKevin Maik Jablonka, Daniele Ongari, Berend Smit
Nature Reviews. Chemistry|August 14, 2025
Real AI advances require collaborationN M Anoop Krishnan, Kevin Maik Jablonka
Nature Communications|June 5, 2026
End-to-end multimodal structure elucidation from raw spectra combining contrastive learning and evolutionary algorithmsAdrian Mirza, Luc Patiny, Kevin Maik Jablonka
Digital Discovery|July 12, 2024
Deep learning-based recommendation system for metal-organic frameworks (MOFs)Xiaoqi Zhang, Kevin Maik Jablonka, Berend Smit
Journal of the American Chemical Society|November 10, 2020
The Role of Machine Learning in the Understanding and Design of MaterialsSeyed Mohamad Moosavi, Kevin Maik Jablonka, Berend Smit
Nature Chemistry|July 6, 2021
Using collective knowledge to assign oxidation states of metal cations in metal-organic frameworksKevin Maik Jablonka, Daniele Ongari, Seyed Mohamad Moosavi, et al.
Chemical Reviews|June 11, 2020
Big-Data Science in Porous Materials: Materials Genomics and Machine LearningKevin Maik Jablonka, Daniele Ongari, Seyed Mohamad Moosavi, et al.
Digital Discovery|May 23, 2025
MOFChecker: a package for validating and correcting metal-organic framework (MOF) structuresXin Jin, Kevin Maik Jablonka, Elias Moubarak, et al.
ACS Central Science|May 1, 2023
An Ecosystem for Digital Reticular ChemistryKevin Maik Jablonka, Andrew S Rosen, Aditi S Krishnapriyan, et al.
Pageof 3

Showing results (1-10 of 28) with videos related to

Sort By:
Pageof 3
Nature Chemistry|April 5, 2022
Making the collective knowledge of chemistry open and machine actionableKevin Maik Jablonka, Luc Patiny, Berend Smit
Journal of Chemical Theory and Computation|August 24, 2019
Applicability of Tail Corrections in the Molecular Simulations of Porous MaterialsKevin Maik Jablonka, Daniele Ongari, Berend Smit
Nature Reviews. Chemistry|August 14, 2025
Real AI advances require collaborationN M Anoop Krishnan, Kevin Maik Jablonka
Nature Communications|June 5, 2026
End-to-end multimodal structure elucidation from raw spectra combining contrastive learning and evolutionary algorithmsAdrian Mirza, Luc Patiny, Kevin Maik Jablonka
Digital Discovery|July 12, 2024
Deep learning-based recommendation system for metal-organic frameworks (MOFs)Xiaoqi Zhang, Kevin Maik Jablonka, Berend Smit
Journal of the American Chemical Society|November 10, 2020
The Role of Machine Learning in the Understanding and Design of MaterialsSeyed Mohamad Moosavi, Kevin Maik Jablonka, Berend Smit
Nature Chemistry|July 6, 2021
Using collective knowledge to assign oxidation states of metal cations in metal-organic frameworksKevin Maik Jablonka, Daniele Ongari, Seyed Mohamad Moosavi, et al.
Chemical Reviews|June 11, 2020
Big-Data Science in Porous Materials: Materials Genomics and Machine LearningKevin Maik Jablonka, Daniele Ongari, Seyed Mohamad Moosavi, et al.
Digital Discovery|May 23, 2025
MOFChecker: a package for validating and correcting metal-organic framework (MOF) structuresXin Jin, Kevin Maik Jablonka, Elias Moubarak, et al.
ACS Central Science|May 1, 2023
An Ecosystem for Digital Reticular ChemistryKevin Maik Jablonka, Andrew S Rosen, Aditi S Krishnapriyan, et al.
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