ArticleNature communications2026
OMNI-P2x universal neural network potential for excited-state simulations.
Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Gradients not needed: ML-driven propagation of nonadiabatic molecular dynamics without reference gradients.Chemical science · 2026Article
- 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
Photo-active molecular systems play an essential role in modern science and technology, finding applications in solar cells, organic light-emitting diodes, reaction catalysis, photodynamic therapy, and beyond. The rational design of photo-responsive molecules requires an understanding of the photophysical and photochemical processes underlying their operation, which can be gained via the first-principles quantum-mechanical calculations that are prohibitively expensive for high-throughput investigations. To break through this limitation, here we introduce OMNI-P2x: a universal neural network potential for molecular excited and ground electronic states. OMNI-P2x can be used, directly or after fine-tuning, to perform a wide range of photophysical and photochemical simulations. OMNI-P2x approaches the accuracy of time-dependent density functional theory methods at a fraction of the computational cost, while being more accurate and faster than established semiempirical methods for excited-state simulations. Here, we demonstrate its use in UV/Vis absorption spectroscopy, real-time photodynamical simulations, and in the rational design of visible-light-absorbing azobenzene systems.
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.