Data-driven Materials Informatics for Optoelectronics: From Natural Language Processing to Predictive Modelling of TADF Molecules
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University of Cambridge
Department of Physics
Department of Physics
Abstract
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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.
China Scholarship Council Cambridge Commonwealth, European and International Trust
China Scholarship Council Cambridge Commonwealth, European and International Trust