Abstract Machine learning (ML) has become a central focus of the computational chemistry community. I will first discuss my personal history in the field. Then I will provide a broader view of how this resurgence in ML interest echoes and advances upon earlier efforts. Although numerous changes have brought about this latest wave, one of the most significant is the increased accuracy and efficiency of low‐cost methods (e. g., density functional theory or DFT) that have made it possible to generate large data sets for ML models. ML has also been used to bypass, guide, or improve DFT. The field of computational chemistry thus finds itself at a crossroads as ML both augments and supersedes traditional efforts. I will present what I believe the role of the computational chemist will be in this evolving landscape, with specific focus on my experience in the development of autonomous workflows in computational materials discovery for open‐shell transition‐metal chemistry.
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A DFT perspective on organometallic lanthanide chemistry
Computational studies of the coordination chemistry and bonding of lanthanides have grown in recent decades as the need for understanding the distinct physical, optical, and magnetic properties of these compounds increased. Density functional theory (DFT) methods offer a favorable balance of computational cost and accuracy in lanthanide chemistry and have helped to advance the discovery of novel oxidation states and electronic configurations. This Frontier article examines the scope and limitations of DFT in interpreting structural and spectroscopic data of low-valent lanthanide complexes, elucidating periodic trends, and predicting their properties and reactivity, presented through selected examples.
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- Award ID(s):
- 2102568
- PAR ID:
- 10544126
- Publisher / Repository:
- RSC
- Date Published:
- Journal Name:
- Dalton Transactions
- Volume:
- 53
- Issue:
- 2
- ISSN:
- 1477-9226
- Page Range / eLocation ID:
- 410 to 417
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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