From Corpus to Innovation: Advancing Organic Solar Cell Design with Large Language Models
Source abstract
Abstract
Advances in machine learning have transformed materials discovery, yet challenges remain due to the lack of informatics-ready data and the complexity of numerical descriptors. Scientific knowledge is scattered across publications, making comprehensive data extraction difficult. This study presents a large language model (LLM)-driven framework to accelerate organic solar cell (OSC) materials discovery by extracting structured data from literature and predicting device performance using natural language embeddings. Trained on a curated dataset of 422 OSC devices, the fine-tuned LLM demonstrated strong predictive accuracy across key performance metrics: power conversion efficiency (PCE, R 2 : 0.87), short-circuit current (J S C , R 2 : 0.82), open-circuit voltage (V O C , R 2 : 0.89), and fill factor (FF, R 2 : 0.59). The models are then used to explore the space of 1.4 million combinations of materials, experimental variables and device architectures. The analysis provides data-driven design guidelines, identifying optimal donor-acceptor combinations and processing conditions that consistently yield higher device performance.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Machine Learning in Materials Science · Organic Electronics and Photovoltaics · Perovskite Materials and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (65)
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