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Meta-learning as a machine learning tool for experimental boosting of sorption properties of ionic liquids
The proposed project involves the development of a methodology for the experimental design of the chemical structure of ionic liquids (as modern absorbent substances), taking into account modern meta-learning computer modeling methods. The methodology will be based on the application of machine learning algorithms (neural networks, including graph neural networks), meta-learning techniques (Model Agnostic Meta Learner), and techniques for the experimental measurement of the sorption properties of ionic liquids (measurement of pharmaceutical solubility by the shake flask method, measurement of gas sorption by the pressure drop method). Expected measurable effects constituting scientific value for both the project leader and the scientific unit where the project will be carried out: 1) a series of 3-5 publications in high impact factor (IF) journals 2) participation in thematic national and international conferences with oral and poster presentations 3) the research results will constitute an essential part of the Ph.D. thesis of the project leader.
Details
- Financial Program Name:
- PRELUDIUM
- Organization:
- Narodowe Centrum Nauki (NCN) (National Science Centre)
- Realisation period:
- 2024-01-12 - 2027-01-11
- Project manager:
- Karol Baran
- Realised in:
- Department of Physical Chemistry
- Request type:
- National Research Programmes
- Domestic:
- Domestic project
- Verified by:
- Gdańsk University of Technology
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