Real-world test: self-healing polymer Diels–Alder reactions.
Functionals that looked fine on BH9 underperform badly—while CYCLO70-validated functionals (ωB97M-V, κPr2SCAN) remain robust. (5/6)

Tested across 93 functionals, errors on CYCLO70 are far larger than on BH9PERI. This dataset captures the worst-case scenarios you might encounter in screening or reactivity modeling. (3/6)

Why CYCLO70?
Popular datasets like BH9 are biased toward “easy” cases. They give an overly optimistic picture of DFT accuracy. CYCLO70 is built to probe the hardest regions of the pericyclic reaction space. (2/6)

Performing a PCA for the errors across the dataset, we demonstrate not only that the errors across different functionals correlate to a significant extent, but also that functionals belonging to the same rung of Jacob’s ladder cluster together in the resulting plot (5/5)

We observe that only one functional, the range-separated hybrid ωB97M-V, reaches ”chemical accuracy” to model barriers and reaction energies; among the double hybrids, PBE-QIDH performs best, and among the hybrids, it is M06-2X and r2SCAN50 that exhibit the lowest errors (4/5)

CYCLO70 is a challenging benchmarking dataset for pericyclic reactions. Testing 93 distinct functionals, we observe that the errors on CYCLO70 are significantly bigger than those on the cycloaddition subset of BH9, the most popular benchmarking set for this reaction class (3/5)

New preprint -- CYCLO70: A New Challenging Pericyclic Benchmarking Set for Kinetics and Thermochemistry Evaluation t.co/O6309jKaJq (1/5)

We first leverage our models to screen a comprehensive reaction space of synthetic diene-dienophile pairs, and subsequently use them to mine a database of commercially available natural products (4/5)

Refining only a small fraction of these profiles with DFT, we can train a robust ML model that predicts reaction characteristics with excellent accuracy. Adding a graph-based model to the workflow for pre-screening enables expansion to reaction spaces of 100k+ reactions (3/5)

In this work, we present a hierarchical workflow that integrates ML with automated reaction profile calculations to efficiently screen DA reaction spaces. Using our in-house TS-tools software, we first rapidly generate reaction profiles at the semi-empirical xTB level (2/5)

New preprint from our group: Screening Diels-Alder reaction space to identify candidate reactions for self-healing polymer applications (1/5)

chemrxiv.org/engage/chemr...

We’re reopening applications! A 5-year position as Data Steward/Research Engineer for the chemistry departments of
@psl-univ.bsky.social is available.

💼 New deadline: February 7th.
📄 More info & application details below.
🔁 Reposts appreciated! 🙌