ArticleAnnual review of materials research2025
Advances in the Design and Discovery of Organic Semiconductors Aided by Machine Learning.
Article in Annual review of materials research, 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.
- Machine Learning in Non-fullerene Organic Solar Cells: Accelerating Discovery, Design, and Understanding.ACS omega · 2026Review
- Machine Learning Potential-Enabled Platform for the In Silico Design of Functional Organic Molecular Crystals.Journal of chemical information and modeling · 2026Article
- Controlling the fate of two triplet states: solid-state annihilator design for photon upconversion.Chemical science · 2026Review
- High-throughput electronic property prediction of cyclic molecules with 3D-enhanced machine learning.Chemical science · 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
3 authors.
Funding
Abstract
Organic semiconductors (OSC) offer the capacity for distinctive and finely tuned electronic, optical, thermal, and mechanical properties, making them of interest across a range of energy generation and storage, sensor, lighting, display, and electronics applications. The pathway from molecular building block design to material, however, is complicated by complex synthesis/processing-structure-property-function relationships that are inherent to OSC. The adoption of artificial intelligence (AI) tools, including the subset of AI referred to as machine learning (ML), into the materials design and discovery pipeline offers significant potential to overcome the multifaceted roadblocks along this pathway. Here, we review recent advances in the application of AI/ML for OSC, with a focus on the development and use of ML. We present a brief primer on ML models and then highlight efforts wherein ML is used to predict molecular and material properties and discover new molecular building blocks and OSC.
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.