{"id":{"repo_id":"cambridge","oai_identifier":"oai:www.repository.cam.ac.uk:1810/395706"},"canonical_url":"https://search.dev.ndltd.org/etd/cambridge/oai:www.repository.cam.ac.uk:1810/395706","repository":{"repo_id":"cambridge","name":"Cambridge University","base_url":"https://api.repository.cam.ac.uk/server/oai/request"},"display":{"title":"Data-driven Materials Informatics for Optoelectronics: From Natural Language Processing to Predictive Modelling of TADF Molecules","abstract":"This 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.","abstract_html":"This 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.","abstract_has_math":false,"creators":["Huang, Dingyun"],"institution":"University of Cambridge","degree_name":"Doctor of Philosophy (PhD)","degree_level":"Doctoral","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":["Cole, Jacqueline"],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-11-19","date_published":"2025-11-19","updated_at":"2026-07-22T22:23:54Z","subjects":["deep learning","language model","machine learning","organic light-emitting diode","text-mining","thermally-activated delayed fluorescence"],"languages":["eng"],"rights":[],"rights_urls":["https://www.repository.cam.ac.uk/bitstreams/8019e4bf-8584-4343-a09b-1c0448ea9a41/download","http://purl.org/NET/rdflicense/allrightsreserved"],"identifier_entries":[]},"links":{"outbound_url":"https://doi.org/10.17863/CAM.125133","outbound_label":"DOI","outbound_source":"dc:identifier.doi"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Cole, Jacqueline"]},{"key":"dc:contributor.sponsor","label":"Sponsor","values":["China Scholarship Council Cambridge Commonwealth, European and International Trust"]},{"key":"dc:creator","label":"Author","values":["Huang, Dingyun"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.issued","label":"Date","values":["2025-11-19"]},{"key":"dc:publisher.institution","label":"Dc Publisher Institution","values":["University of Cambridge"]},{"key":"dc:relation.isreferencedby.uri","label":"Dc Relation Isreferencedby URI","values":["https://www.repository.cam.ac.uk/handle/1810/395706"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["Doctoral"]},{"key":"dc:type.qualificationname","label":"Dc Type Qualificationname","values":["Doctor of Philosophy (PhD)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["deep learning","language model","machine learning","organic light-emitting diode","text-mining","thermally-activated delayed fluorescence"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["https://www.repository.cam.ac.uk/bitstreams/8019e4bf-8584-4343-a09b-1c0448ea9a41/download","http://purl.org/NET/rdflicense/allrightsreserved"]},{"key":"dc:rights.embargodate","label":"Dc Rights Embargodate","values":["2027-01-22"]},{"key":"dc:rights.embargotype","label":"Dc Rights Embargotype","values":["embargo"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.doi","label":"DOI","values":["https://doi.org/10.17863/CAM.125133"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://www.repository.cam.ac.uk/bitstreams/45cedb2a-1872-4d02-ad98-61270a0a931f/download"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["This 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."]},{"key":"dc:format.checksum.md5","label":"Dc Format Checksum Md5","values":["47eb040e7d991b124e30e05d5a83fe18","87eda9de84448d1f82354d60eee3eb5f"]},{"key":"dc:title","label":"Title","values":["Data-driven Materials Informatics for Optoelectronics: From Natural Language Processing to Predictive Modelling of TADF Molecules"]}]}],"canonical_facts":{"dc:contributor.advisor":["Cole, Jacqueline"],"dc:contributor.sponsor":["China Scholarship Council Cambridge Commonwealth, European and International Trust"],"dc:creator":["Huang, Dingyun"],"dc:date.issued":["2025-11-19"],"dc:description.abstract":["This 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:format.checksum.md5":["47eb040e7d991b124e30e05d5a83fe18","87eda9de84448d1f82354d60eee3eb5f"],"dc:identifier.doi":["https://doi.org/10.17863/CAM.125133"],"dc:identifier.uri":["https://www.repository.cam.ac.uk/bitstreams/45cedb2a-1872-4d02-ad98-61270a0a931f/download"],"dc:language":["eng"],"dc:publisher.institution":["University of Cambridge"],"dc:relation.isreferencedby.uri":["https://www.repository.cam.ac.uk/handle/1810/395706"],"dc:rights":["https://www.repository.cam.ac.uk/bitstreams/8019e4bf-8584-4343-a09b-1c0448ea9a41/download","http://purl.org/NET/rdflicense/allrightsreserved"],"dc:rights.embargodate":["2027-01-22"],"dc:rights.embargotype":["embargo"],"dc:subject":["deep learning","language model","machine learning","organic light-emitting diode","text-mining","thermally-activated delayed fluorescence"],"dc:title":["Data-driven Materials Informatics for Optoelectronics: From Natural Language Processing to Predictive Modelling of TADF Molecules"],"dc:type":["Thesis"],"dc:type.qualificationlevel":["Doctoral"],"dc:type.qualificationname":["Doctor of Philosophy (PhD)"]},"updated_at":"2026-07-22T22:23:54Z"}