Home ›
Entities
› academia
› Machine Learning Enables Highly Accurate Predictions of Photophysical Properties of Organic Fluorescent Materials: Emission Wavelengths and Quantum Yields
Machine Learning Enables Highly Accurate Predictions of Photophysical Properties of Organic Fluorescent Materials: Emission Wavelengths and Quantum Yields
Research article (Journal of Chemical Information and Modeling, 2021) · cited 159× · AI/ML
Machine Learning Enables Highly Accurate Predictions of Photophysical Properties of Organic Fluorescent Materials: Emission Wavelengths and Quantum Yields
Summary
Machine Learning Enables Highly Accurate Predictions of Photophysical Properties of Organic Fluorescent Materials: Emission Wavelengths and Quantum Yields is a scholarly article[1].
Key Facts
Machine Learning Enables Highly Accurate Predictions of Photophysical Properties of Organic Fluorescent Materials: Emission Wavelengths and Quantum Yields's instance of is recorded as scholarly article[2].
References
Programmatic citations — every numbered marker resolves to a verifiable graph row below.
Use these citations when quoting this entity in research, articles, AI prompts, or wherever provenance matters. We aggregate Wikidata + Wikipedia + authoritative open-data sources; the stitched, scored, cross-referenced view is what 4ort.xyz contributes.
APA4ort.xyz Knowledge Graph. (2026). Machine Learning Enables Highly Accurate Predictions of Photophysical Properties of Organic Fluorescent Materials: Emission Wavelengths and Quantum Yields. Retrieved May 24, 2026, from https://4ort.xyz/entity/machine-learning-enables-highly-accurate-predictions-of-photophysical-properties-of-organic-fluorescent-materials-emissi
MLA“Machine Learning Enables Highly Accurate Predictions of Photophysical Properties of Organic Fluorescent Materials: Emission Wavelengths and Quantum Yields.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/machine-learning-enables-highly-accurate-predictions-of-photophysical-properties-of-organic-fluorescent-materials-emissi.
BibTeX@misc{4ortxyz_machine-learning-enables-highly-accurate-predictions-of-photophysical-properties-of-organic-fluorescent-materials-emissi_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Machine Learning Enables Highly Accurate Predictions of Photophysical Properties of Organic Fluorescent Materials: Emission Wavelengths and Quantum Yields}}, year = {2026}, url = {https://4ort.xyz/entity/machine-learning-enables-highly-accurate-predictions-of-photophysical-properties-of-organic-fluorescent-materials-emissi}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Machine Learning Enables Highly Accurate Predictions of Photophysical Properties of Organic Fluorescent Materials: Emission Wavelengths and Quantum Yields — https://4ort.xyz/entity/machine-learning-enables-highly-accurate-predictions-of-photophysical-properties-of-organic-fluorescent-materials-emissi (retrieved 2026-05-24)
