ArticleScientific data2025
Surface Hopping Nested Instances Training Set for Excited-state Learning.
Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- From reproducibility to comparability in computational photochemistry: lessons from the PhotoChem explorer prototype.Faraday discussions · 2026Article
- Gradients not needed: ML-driven propagation of nonadiabatic molecular dynamics without reference gradients.Chemical science · 2026Article
- Machine learning for nonadiabatic molecular dynamics: best practices and recent progress.Chemical science · 2025Review
- Surface Hopping Nested Instances Training Set for Excited-state Learning.Scientific data · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Theoretical studies of molecular photochemistry and photophysics are essential for understanding fundamental natural processes but rely on computationally demanding quantum chemical calculations. This complexity limits both direct simulations and the development of machine learning (ML) models trained on this data. To address this, we introduce SHNITSEL, a data repository containing 418,870 ab-initio data points of nine organic molecules in their ground and electronically excited states. Each data point includes high-accuracy quantum chemical properties such as energies, forces, and dipole moments in the ground state and electronically excited singlet or triplet states as well as properties that arise from the coupling of electronic states, namely nonadiabatic couplings, transition dipoles, or spin-orbit couplings. Generated with state-of-the-art methods, SHNITSEL provides a robust benchmark for ML models and facilitates the development of ML-based approaches for excited state properties.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.