FAIR data and inclusive science to enable clean energy
Fusion is the process of combining two light atomic nuclei to form a single heavier one while releasing massive amounts of energy. The Sun and all stars are powered through fusion, which makes it the Universe’s preferred method of producing energy. Recent breakthroughs in fusion research have led to the US government’s Bold Vision for Commercial Fusion Energy and the remarkable growth of the global fusion industry.
To accelerate the development of fusion-powered reactors on Earth, the US Department of Energy has selected a collaboration among researchers at MIT (as lead), Auburn University, William & Mary, University of Wisconsin-Madison, and the HDF Group to receive close to $5 million in funding. The project will develop a platform where data from different fusion devices, including Auburn’s Compact Toroidal Hybrid (CTH), is managed according to Findable, Interoperable, Accessible, and Reusable (FAIR) standards and UNESCO’s Open Science (OS) recommendations. The data will also be adapted for use with machine learning (ML) tools. The platform’s databases will be built using MDSplusML, an upgraded version of the MDSplus open-source software developed by MIT’s Plasma Science and Fusion Center researchers in the ’80s to catalogue the results of the Alcator C-Mod’s experiments. Today, nearly 40 fusion research institutes use MDSplus to store and provide external access to their fusion data. The release of MDSplusML will enable free exchange of data and models across institutions, thus speeding up progress in fusion research.
The Auburn portion of the project is led by Dr. Evdokiya (Eva) Kostadinova, an Assistant Professor at the Physics Department, who specializes in interdisciplinary plasma research. Kostadinova and her students will collaborate with Dr. David Maurer, an Associate Professor at the Physics Department and CTH head. In Auburn’s CTH experiment, magnetic fields can be shaped in different ways to confine a hot plasma – the state of matter in which fusion reactions happen. Open-sourcing CTH data and adapting it for use with ML tools will allow researchers to explore various concepts for fusion reactors. On the significance of this award, Kostadinova comments, “Fusion research has made remarkable progress, which is evident from multiple exciting results from experiments worldwide. However, true breakthroughs rely on strong collaborations, committed to open science and diversity of viewpoints. This project will enable such collaborations and will allow us to use machine learning to uncover fundamental science hidden in big datasets.”
In addition to being a cross-institutional collaboration between four universities and an industry partner, the project also includes a strong focus on workforce development. With four out of five PIs being women scientists, the team hopes to inspire and encourage diversity in the next generation of fusion scientists. To make this reality, in each year of the project, the College of William and Mary will host a summer school where undergraduate students will learn how to employ ML techniques in fusion research. On the role of diverse leadership, the MIT lead, Dr. Cristina Rea, says, “Having the opportunity to lead such an important project is extremely meaningful, and I feel a responsibility to show that women are leaders in STEM. We have an incredible team, strongly motivated to improve our fusion ecosystem and to contribute making fusion energy a reality.”
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