Enhancing the stability of organic photovoltaics through machine learning
- Submitting institution
-
Bangor University / Prifysgol Bangor
- Unit of assessment
- 12 - Engineering
- Output identifier
- UoA12_60
- Type
- D - Journal article
- DOI
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10.1016/j.nanoen.2020.105342
- Title of journal
- Nano Energy
- Article number
- 105342
- First page
- 105342
- Volume
- 78
- Issue
- -
- ISSN
- 2211-2855
- Open access status
- Compliant
- Month of publication
- September
- Year of publication
- 2020
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- Yes
- Number of additional authors
-
5
- Research group(s)
-
-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This work builds on the contributions and involvement of W. Teahan with the research team that developed the machine learning workbench WEKA (New Zealand) and ongoing research with text mining and natural language engineering. It has inspired the current development of a collaborative tool for the automatic mining of scientific literature that can enable a new paradigm of scientific breakthroughs resulting from the application of machine-assisted learning to solving problems in the engineering domain. This work contributed to an invited ‘hot topics’ talk at the ISOS 2019 conference by one of the co-authors of the paper. (https://www.isos12.kit.edu/downloads/Program_ISOS12.pdf)
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
