ArticleNature communications2021
Active discovery of organic semiconductors.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 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
24 citing papers in PubMed.
- Machine-Learning-Guided Discovery of Boron-Free Narrowband Blue Thermally Activated Delayed Fluorescence Emitters.Angewandte Chemie (International ed. in English) · 2026Article
- Donor-Acceptor Derivatives of Indolo[3,2-b]indole and Benzothieno[3,2-b]benzothiophene: Similar Annulated Structures but Divergent Properties.Molecules (Basel, Switzerland) · 2026Article
- Machine Learning in Non-fullerene Organic Solar Cells: Accelerating Discovery, Design, and Understanding.ACS omega · 2026Review
- Fingerprinting organic molecules for the inverse design of two-dimensional hybrid perovskites with target energetics.Science advances · 2026Article
- PAL - parallel active learning for machine-learned potentials.Digital discovery · 2025Article
- Advancing genetic engineering with active learning: theory, implementations and potential opportunities.Briefings in bioinformatics · 2025Review
- Advances in the Design and Discovery of Organic Semiconductors Aided by Machine Learning.Annual review of materials research · 2025Article
- Exploring the Interplay of Lattice Dynamics and Charge Transport in Organic Semiconductors: Progress Toward Rational Phonon Engineering.Angewandte Chemie (International ed. in English) · 2025Review
- Tailoring Super-Performed Chemo-Sensor via Simulation-Modeling and MEMS-Screening.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Integration of Functional Materials in Photonic and Optoelectronic Technologies for Advanced Medical Diagnostics.Biosensors · 2025Review
- Active learning streamlines development of high performance catalysts for higher alcohol synthesis.Nature communications · 2024Article
- Exploration and development of molecule-based printed electronics materials: an integrated approach using experimental, computational, and data sciences.Science and technology of advanced materials · 2024Review
- Statistical analysis of interatomic transfer integrals for exploring high-mobility organic semiconductors.Science and technology of advanced materials · 2024Article
- Organic Electronics in Biosensing: A Promising Frontier for Medical and Environmental Applications.Biosensors · 2023Review
- Recent Progress in Flexible and Wearable All Organic Photoplethysmography Sensors for SpOAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2023Review
- Swarm Smart Meta-Estimator for 2D/2D Heterostructure Design.Journal of chemical information and modeling · 2023Article
- Machine learning of atomic force microscopy images of organic solar cells.RSC advances · 2023Article
- Poly(amidoamine) Dendrimer as an Interfacial Dipole Modification in Crystalline Silicon Solar Cells.The journal of physical chemistry letters · 2023Article
- Charge Localization in Acene Crystals fromThe journal of physical chemistry letters · 2023Article
- Conductivity in Thin Films of Transition Metal Coordination Complexes.ACS applied energy materials · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The versatility of organic molecules generates a rich design space for organic semiconductors (OSCs) considered for electronics applications. Offering unparalleled promise for materials discovery, the vastness of this design space also dictates efficient search strategies. Here, we present an active machine learning (AML) approach that explores an unlimited search space through consecutive application of molecular morphing operations. Evaluating the suitability of OSC candidates on the basis of charge injection and mobility descriptors, the approach successively queries predictive-quality first-principles calculations to build a refining surrogate model. The AML approach is optimized in a truncated test space, providing deep methodological insight by visualizing it as a chemical space network. Significantly outperforming a conventional computational funnel, the optimized AML approach rapidly identifies well-known and hitherto unknown molecular OSC candidates with superior charge conduction properties. Most importantly, it constantly finds further candidates with highest efficiency while continuing its exploration of the endless design space.
Identifiers
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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.