| Autor: |
Xie T; School of Photovoltaic and Renewable Energy Engineering, University of New South Wales, Kensington, NSW 2052, Australia.; GreenDynamics Pty. Ltd., Kensington, NSW 2052, Australia., Wan Y; Department of Linguistics and Translation, City University of Hong Kong, 83 Tat Chee Ave, Kowloon Tong, Hong Kong.; GreenDynamics Pty. Ltd., Kensington, NSW 2052, Australia., Wang H; School of Photovoltaic and Renewable Energy Engineering, University of New South Wales, Kensington, NSW 2052, Australia., Østrøm I; School of Photovoltaic and Renewable Energy Engineering, University of New South Wales, Kensington, NSW 2052, Australia., Wang S; School of Photovoltaic and Renewable Energy Engineering, University of New South Wales, Kensington, NSW 2052, Australia.; GreenDynamics Pty. Ltd., Kensington, NSW 2052, Australia., He M; School of Photovoltaic and Renewable Energy Engineering, University of New South Wales, Kensington, NSW 2052, Australia., Deng R; School of Photovoltaic and Renewable Energy Engineering, University of New South Wales, Kensington, NSW 2052, Australia., Wu X; School of Photovoltaic and Renewable Energy Engineering, University of New South Wales, Kensington, NSW 2052, Australia., Grazian C; DARE ARC Training Centre in Data Analytics for Resources and Environments, South Eveleigh, NSW 2015, Australia.; School of Mathematics and Statistics, University of Sydney, Camperdown, NSW 2006, Australia., Kit C; Department of Linguistics and Translation, City University of Hong Kong, 83 Tat Chee Ave, Kowloon Tong, Hong Kong., Hoex B; School of Photovoltaic and Renewable Energy Engineering, University of New South Wales, Kensington, NSW 2052, Australia. |
| Abstrakt: |
The scientific literature contains valuable information that can be used for future applications, but manual analysis presents challenges due to its size and disciplinary boundaries. The prevailing solution involves natural language processing (NLP) techniques such as information retrieval. Nonetheless, existing automated systems primarily provide either statistically based shallow information or deep information without traceability, thereby falling short of delivering high-quality and reliable insights. To address this, we propose an innovative approach of leveraging sentiment information embedded within the literature to track the opinions toward materials. In this study, we integrated material knowledge into text representation and constructed opinion data sets to hierarchically train deep learning models, named as Scientific Sentiment Network (SSNet). SSNet can effectively extract knowledge from the energy material literature and accurately categorize expert opinions into challenges and opportunities (94% and 92% accuracy, respectively). By incorporating sentiment features determined by SSNet, we can predict the ranking of emerging thermoelectric materials with a 70% correlation to experimental outcomes. Furthermore, our model achieves a commendable 68% accuracy in predicting suitable nanomaterials for atomic layer deposition (ALD) over time. These promising results offer a practical framework to extract and synthesize knowledge from the scientific literature, thereby accelerating research in the field of nanomaterials. |
