Large yet bounded: Spin gap ranges in carbenes
Max Schwilk, Diana N. Tahchieva, and O. Anatole von Lilienfeld

TL;DR
This study systematically explores the electronic structure of 8,000 diverse carbene molecules, revealing a universal upper limit for the singlet-triplet gap and providing a comprehensive database of high-accuracy quantum calculations.
Contribution
It introduces a large, systematic computational analysis of carbene electronic structures, establishing a universal upper limit for the singlet-triplet gap and creating a valuable quantum chemistry database.
Findings
Universal upper limit of ~30 kcal/mol for singlet-triplet gap in carbenes
Large vertical and adiabatic spin gap ranges observed within carbene classes
Development of a comprehensive QMspin database with ~13,000 high-accuracy calculations
Abstract
Despite its relevance for chemistry, the electronic structure of free carbenes throughout chemical space has not yet been studied in a systematic manner. We explore a large and systematic carbene chemical space consisting of eight thousand diverse and common carbene scaffolds in their singlet and triplet state computed at controlled accuracy (higher order multireference level of theory) and with verified carbene character in the electronic structure. Originating in strong electron correlation, a hard upper limit for the singlet-triplet gap is found to emerge at around 30 kcal/mol for all the carbene classes in this chemical space. We also observe large vertical and adiabatic spin gap ranges within many carbene classes (100 and 60 kcal/mol, respectively), and we report novel relationships between compositional, structural, and electronic degrees of freedom. Our QMspin data base…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsCatalysis and Oxidation Reactions · Cyclopropane Reaction Mechanisms · Machine Learning in Materials Science
