Home ›
Entities
› academia
› Deep transfer learning for predicting frontier orbital energies of organic materials using small data and its application to porphyrin photocatalysts
Deep transfer learning for predicting frontier orbital energies of organic materials using small data and its application to porphyrin photocatalysts
Research article (Physical Chemistry Chemical Physics, 2023) · cited 16× · AI/ML
Deep transfer learning for predicting frontier orbital energies of organic materials using small data and its application to porphyrin photocatalysts
Summary
Deep transfer learning for predicting frontier orbital energies of organic materials using small data and its application to porphyrin photocatalysts is a scholarly article[1].
Key Facts
Deep transfer learning for predicting frontier orbital energies of organic materials using small data and its application to porphyrin photocatalysts'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). Deep transfer learning for predicting frontier orbital energies of organic materials using small data and its application to porphyrin photocatalysts. Retrieved May 24, 2026, from https://4ort.xyz/entity/deep-transfer-learning-for-predicting-frontier-orbital-energies-of-organic-materials-using-small-data-and-its-applicatio
MLA“Deep transfer learning for predicting frontier orbital energies of organic materials using small data and its application to porphyrin photocatalysts.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/deep-transfer-learning-for-predicting-frontier-orbital-energies-of-organic-materials-using-small-data-and-its-applicatio.
BibTeX@misc{4ortxyz_deep-transfer-learning-for-predicting-frontier-orbital-energies-of-organic-materials-using-small-data-and-its-applicatio_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Deep transfer learning for predicting frontier orbital energies of organic materials using small data and its application to porphyrin photocatalysts}}, year = {2026}, url = {https://4ort.xyz/entity/deep-transfer-learning-for-predicting-frontier-orbital-energies-of-organic-materials-using-small-data-and-its-applicatio}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Deep transfer learning for predicting frontier orbital energies of organic materials using small data and its application to porphyrin photocatalysts — https://4ort.xyz/entity/deep-transfer-learning-for-predicting-frontier-orbital-energies-of-organic-materials-using-small-data-and-its-applicatio (retrieved 2026-05-24)
