Accelerated chemical science with AI

Abstract:
        The ASLLA Symposium focused on accelerating chemical science with AI. Discussions on data, new applications, algorithms, and education were summarized. Recommendations for researchers, educators, and academic bodies were provided.

SEEK ID: https://publications.h-its.org/publications/1772

Filename: 2024_Back_DD.pdf 

Format: PDF document

Size: 582 KB

SEEK ID: https://publications.h-its.org/publications/1772

DOI: 10.1039/d3dd00213f

Research Groups: Computational Carbon Chemistry

Publication type: Journal

Journal: Digital Discovery

Citation: Digital Discovery 3(1):23-33

Date Published: 17th Jan 2024

Registered Mode: by DOI

Authors: Seoin Back, Alán Aspuru-Guzik, Michele Ceriotti, Ganna Gryn'ova, Bartosz Grzybowski, Geun Ho Gu, Jason Hein, Kedar Hippalgaonkar, Rodrigo Hormázabal, Yousung Jung, Seonah Kim, Woo Youn Kim, Seyed Mohamad Moosavi, Juhwan Noh, Changyoung Park, Joshua Schrier, Philippe Schwaller, Koji Tsuda, Tejs Vegge, O. Anatole von Lilienfeld, Aron Walsh

help Submitter
Citation
Back, S., Aspuru-Guzik, A., Ceriotti, M., Gryn'ova, G., Grzybowski, B., Gu, G. H., Hein, J., Hippalgaonkar, K., Hormázabal, R., Jung, Y., Kim, S., Kim, W. Y., Moosavi, S. M., Noh, J., Park, C., Schrier, J., Schwaller, P., Tsuda, K., Vegge, T., … Walsh, A. (2024). Accelerated chemical science with AI. In Digital Discovery (Vol. 3, Issue 1, pp. 23–33). Royal Society of Chemistry (RSC). https://doi.org/10.1039/d3dd00213f
Activity

Views: 1891   Downloads: 1

Created: 23rd Jan 2024 at 09:59

Last updated: 11th Mar 2024 at 13:31

help Tags

This item has not yet been tagged.

help Attributions

None

Powered by
(v.1.15.2)
Copyright © 2008 - 2024 The University of Manchester and HITS gGmbH