MOLTEN SALT SELECTION | REACTOR DESIGN
“Then the machine learning models can be used to run the same length scales, which is required for these modern solve simulations, but with the same accuracy as the first principles ab initio approach,” he says. “We employ the partially trained machine learning model with a fixed number of compositions to force it to go to a region it has not seen using metadynamics. We break the potential multiple times to make sure that it captures all these different regions which is not explicitly included in the training data but then we incorporate those in the re-training process. With such a sampling it can interpolate from there an infinite number of combinations. “Instead of changing the composition, we mimic all
the scenarios which correspond to composition change and then we iteratively incorporate that into the machine learning model,” says Sivaraman. This approach means that rather than being fitted for one or two specific molten salt mixture compositions, the transferable model can be applied to multiple mixtures. The model is then able to make its predictions based on these principles, rather than a knowledge-based data set. Having developed the model the machine learning simulations were run using high performance computing resources at the Argonne Leadership Computing Facility (ALCF).
As Sivaraman says “We didn’t train the model with
examples of that sweet spot (i.e., eutectic) composition, where you get the right melting point. Our model managed to predict that sweet spot, even without the corresponding training input.” For the molten salt, while it is possible to mix them in
different ratios and have almost infinite combinations of mixtures it is possible to use the model to explore promising new salt compositions. “We show that you can indeed do that with machine
learning models and then validate that data with physical experiments which makes science more rigorous. What this means is that you can discover salt compositions that are most relevant to the application, since the model can work in all those compositions even where you’ve not explicitly trained with actual data,” notes Sivaraman. Researchers use the powerful X-rays at the APS to look
closely at the structure of specific salt mixtures using high energy X-ray diffraction to validate the model. APS is a unique tool for these types of measurements and the facility’s 6-ID-D beamline was used to verify the AI analysis. “It’s typically very difficult for a simulation to match the readings from instruments, especially for high temperature models but when we compare the results that we get from this simulation to instrument readings from high energy diffraction it matches very well,” Guo noted, adding: “We used experimental results to validate our simulation. At the same time, the simulation results provided us more details about which salts to study further. They work with each other.” Guo adds: “This allows us to study multiple compositions at the same time.”
Increasing complexity With the possible composition for reactor molten salts so enormous, it is almost impossibly expensive and time consuming to try to produce experimental data for every possible composition. The time and cost associated with real-world experiments make it desirable to narrow the field of candidates that undergo inspection so instead,
One of the major problems in machine learning research is it doesn’t work beyond the training data set. This is a topic which many researchers in the general machine learning community are grappling with. How do you make sure these machine learning tools work in regions beyond the training data? The problem here is how to incorporate all the training data for the mixtures where you have not trained the model
the scientists turned to AI as the latest machine learning models can understand things they haven’t been shown and can extrapolate valuable data. “We have already validated six different compositions
where it was never trained. It works and we even have experimental validation for the eutectic, which is an important first result in this domain of machine learning,” says Sivaraman, who adds: “At the moment the research is focused on validating the model that has been devised. Then it will be deployed in a bid to identify desirable salt mixtures for use in reactors”. Indeed, now that the researchers have shown this
approach can work, the next step is to work with even more complex data that more closely reflects the actual environment of an operating molten salt reactor. “A molten salt reactor is quite a dynamic environment. The conditions change over time, and sometimes impurities can get into the salt,” Guo said. “We want to introduce a tiny amount of these impurities to see whether the model can predict how that affects the overall structure of molten salts and their properties.” In addition when impurities go into the salt, it starts
to corrode those high-temperature alloys, such the special alloy developed for the molten salt reactor called Hastelloy-N. These materials can also enter the salt. Furthermore, the model must also take into account the decay products as the reactor is operated and the uranium changes into different materials with different densities. Once the scientists have fully validated the model, the
goal is to be enter a series of desirable characteristics such as density or heat capacity and melting point and the model will come up with the correct composition. “In the next step, we’re going to show we can pick
random molten salts from the periodic table, mix them together and create models and it works really well. For some of them, there have not even been any experiments until now. That is the dream. Pick three or two different salts from the periodic table and then make a model for that and create conditions like the different melting points for that and figuring that out without doing a physical experiment. We have that capability now,” Sivaraman concludes. The key to this project is not just finding a salt
combination that has the desirable density and melting point characteristics, it’s determining how it is actually going to operate within a reactor system. ■
* GAP was first proposed and developed by Prof. Gábor Csányi at University of Cambridge
www.neimagazine.com | March 2023 | 33
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