This week Future Energy Systems hosted its first Interdisciplinary Lunch and Learn, Machine Learning and Models: How we find optimal materials for Solar and CCS technologies. The session brought together two research groups from different themes and faculties that had never previously had an opportunity to collaborate.
Scientists from Principal Investigator Arthur Mar’s project High-throughput Materials Discovery through Materials Genomics opened the session, outlining machine learning at a high level and exploring their uses for it in solar cell development, as well as a variety of other applications.
The engineering team from the project Post Combustion Capture of CO2 using Solid Sorbents, led by Principal Investigator Arvind Rajendran, then elaborated on their methods for using machine learning to simplify the complex modeling needed to identify new materials and processes for the capture of carbon dioxide produced by energy generation.
Over the course of the hour, a full audience including external stakeholders and representatives from numerous Future Energy Systems themes learned how the methods of machine learning can play a role in solving problems across the energy spectrum. The presenting groups also gained access to new perspectives, and opportunities for future cooperation.
Future Energy Systems places a strong emphasis on interdisciplinary collaboration and will continue to encourage interactions between teams across different themes and faculties. The next Interdisciplinary Lunch and Learn will take place in June and feature Future Energy Systems resource economists and systems engineers discussing their methods for assessing the potential viability and impacts of new energy technologies.
For information about that session, stay tuned to this website or subscribe for updates.
In order of appearance: