ArticleJournal of chemical information and modeling2024
Automatic Prediction of Peak Optical Absorption Wavelengths in Molecules Using Convolutional Neural Networks.
Article in Journal of chemical information and modeling, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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
13 citing papers in PubMed.
- Multiobjective Fluorescent Molecule Design with a Data-Physics Dual-Driven Generative Framework.JACS Au · 2026Article
- Nanomaterials for In Vivo Photoacoustic Sensing: Emerging Strategies and Future Outlook.ACS sensors · 2026Review
- Engineering of Optoelectronic Devices for Renewable Energy Applications.Micromachines · 2026Review
- Machine-Learning Predictions of Photoluminescence in Molecules Exhibiting Thermally Activated Delayed Fluorescence with Implicit Experimental Validation.Journal of chemical information and modeling · 2026Article
- A conformational benchmark for optical property prediction with solvent-aware graph neural networks.Communications chemistry · 2026Article
- Enhanced Prediction of Absorption and Emission Wavelengths of Organic Compounds through Hybrid Graph Neural Network Architectures.ACS omega · 2025Article
- Machine Learning-Driven Design of Fluorescent Materials: Principles, Methodologies, and Future Directions.Nanomaterials (Basel, Switzerland) · 2025Review
- Deep learning for property prediction of natural fiber polymer composites.Scientific reports · 2025Article
- Automatic Prediction of Molecular Properties Using Substructure Vector Embeddings within a Feature Selection Workflow.Journal of chemical information and modeling · 2025Article
- Machine-Learning Predictions of Critical Temperatures from Chemical Compositions of Superconductors.Journal of chemical information and modeling · 2024Article
- Predictive Modeling of High-Entropy Alloys and Amorphous Metallic Alloys Using Machine Learning.Journal of chemical information and modeling · 2024Article
- Machine-Learning Prediction of Curie Temperature from Chemical Compositions of Ferromagnetic Materials.Journal of chemical information and modeling · 2024Article
- Automatic Prediction of Band Gaps of Inorganic Materials Using a Gradient Boosted and Statistical Feature Selection Workflow.Journal of chemical information and modeling · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Molecular design depends heavily on optical properties for applications such as solar cells and polymer-based batteries. Accurate prediction of these properties is essential, and multiple predictive methods exist, from
Indexed as
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.