Data-driven Materials Informatics for Optoelectronics: From Natural Language Processing to Predictive Modelling of TADF Molecules

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dc.creatorHuang, Dingyun
dc.date2026-01-22T11:25:44Z
dc.date2025-11-19
dc.date.accessioned2026-08-03T03:36:20Z
dc.descriptionThis thesis addresses the development and application of data-driven approaches to materials informatics for optoelectronics, with a focus on thermally-activated delayed fluorescence (TADF). Chapter 1 provides an introduction to the background and recent progress in data-driven methods in materials sciences and thermally-activated delayed fluorescence. Chapter 2 reviews the natural language processing techniques and language modelling methods that were used throughout the thesis. Chapter 3 demonstrates a pipeline for the extraction of four organic TADF molecule property data from the literature, namely, maximum emission wavelength (𝜆EM ), photolu- minescence quantum yield (PLQY), singlet-triplet energy splitting (Δ𝐸ST ), and delayed life- time (𝜏D ). The pipeline affords a database of 25,482 data records with a collective precision of 82%. Chapter 4 describes a cost-efficient approach to pre-training “optoelectronics-aware” language models via domain-adaptative pre-training (DAPT). Three language models, OE- ALBERT, OE-BERT, and OE-RoBERTa, are produced using this approach. They are also fine-tuned to perform tasks of text-classification, question-answering, and text embedding. Chapter 5 details an end-to-end workflow that produces a data-driven predictor for the PL wavelengths of organic TADF molecules using molecular SMILES strings as its input. The workflow utilizes techniques developed in Chapter 3 and 4 to collect training data. The predictor achieves accurate PL wavelength estimation on an out-of-sample test set with a mean absolute error of 0.13 eV. Chapter 6 concludes the work and discusses potential directions for future research.
dc.descriptionChina Scholarship Council Cambridge Commonwealth, European and International Trust
dc.formatapplication/pdf
dc.identifierhttps://www.repository.cam.ac.uk/handle/1810/395706
dc.identifierhttps://doi.org/10.17863/CAM.125133
dc.identifier.urihttps://repo.dare.co.zw/handle/123456789/178522
dc.languageeng
dc.publisherUniversity of Cambridge
dc.publisherDepartment of Physics
dc.rightsAll rights reserved
dc.rightshttp://purl.org/NET/rdflicense/allrightsreserved
dc.subjectdeep learning
dc.subjectlanguage model
dc.subjectmachine learning
dc.subjectorganic light-emitting diode
dc.subjecttext-mining
dc.subjectthermally-activated delayed fluorescence
dc.titleData-driven Materials Informatics for Optoelectronics: From Natural Language Processing to Predictive Modelling of TADF Molecules
dc.typeThesis
dc.typeDoctoral
dc.typeDoctor of Philosophy (PhD)

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