Reaction: The challenge of open-shell transition metal catalysis in “systems chemistry”
Name
Kulik_Chem_2024.pdf
Description
Accepted version
Size
55.23 KB
Format
Adobe PDF
Checksum (MD5)
a569a147a151257025e217b55afd3d79
Author(s)
Kulik, Heather J
Date Issued
August 8, 2024
Journal
Chem
Publisher
Elsevier BV
Citation
Kulik, Heather J. 2024. "Reaction: The challenge of open-shell transition metal catalysis in “systems chemistry”." Chem, 10 (8).
Version
Author's final manuscript
Abstract
Data-driven methods have transformed the scale at which chemical
transformations are being explored. This includes novel machine learning models for
retrosynthesis, reaction prediction, and small-molecule generation, to name a few. Novel
datasets from high-throughput computation (e.g., with first-principles density functional
theory) as well as high-throughput experimentation or extraction from the literature are
dramatically increasing the scale at which new compounds are discovered as well as the
benefits that can be reaped from deep learning models.
MIT Department
Massachusetts Institute of Technology. Department of Chemical Engineering
Massachusetts Institute of Technology. Department of Chemistry
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
10.1016/j.chempr.2024.06.026
