REACTOR DESIGN | MOLTEN SALT SELECTION


V “Previously, if we perform experiments to explore these characteristics we have to prepare very many different compositions and look at the structure of each one under high energy X-ray, which is time consuming,” says Guo, adding: “Also, working with high temperature molten salt containing radioactive elements is very challenging and very costly because we have to have specialised staff to operate the instruments.” However, another approach is to use computer modelling of the salt using different technologies.


Taking modelling to the next step The traditional commercial technology for modelling these kinds of interactions is molecular dynamic simulation. There are two types of that simulation. One is classical molecular dynamics (MD) based on empirical forcefield, in which a very large cell containing a thousand to ten thousand atoms is modelled. “We can get a rough idea of what’s going on with the properties of this cell but it’s not so accurate because it uses some approximations,” says Guo.


Another approach is ab initio MD, which uses density function theory (DFT) to model a very small cell of maybe a hundred atoms. “The advantage of that is very accurate but it can only predict behaviours of a very small cell, so we don’t know what’s happening on the longer range,” notes Guo. In a bid to address the problems associated with either of these approaches, researchers at Argonne developed a machine learning based Gaussian approximation potential (GAP) capable of learning many-body interactions directly from DFT*. It combines the advantages of these two computer simulations by simulating a very large cell with tens of thousands of atoms, but can still maintain the ab initio accuracy. “This is a great advantage compared to both of these


other techniques,” says Guo. Their research study set out to determine whether computer simulations driven


by machine learning could guide and refine real-world experiments that are then conducted at the Advanced Photon Source (APS), a DOE Office of Science user facility. Although initially modelled for specific molten lithium-


potassium chloride (LiCl-KCl) salt combination, the key to the AI tool is the composition-transferable Gaussian approximation potential (GAP), as Guo says: “I think unique to this research is the ability to have a composition transferable potential, which means we can predict compositions that we didn’t introduce in the training set.” While machine learning typically involves training a


computer to analyse a situation based on existing data, in this case the researchers did not have validated examples that would normally be used by the machine model to learn. “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?,” says Ganesh Sivaraman, Assistant Computational Scientist at the Data Science and Learning Division at Argonne. “The problem here is how to incorporate all the training data for the mixtures where you have not trained the model,” he adds. Building on previous modelling work, the researchers used active learning to create a transferable model to analyse molten salts. An accurate GAP was active-learned from only around 1100 training configurations drawn from 10 unique mixture compositions. This data was enriched with metadynamics. “What machine learning can do is learn the underlying non-linearity, how do the atoms interact within the neighbourhood. That is learned from the training data set which would come from the most accurate data. We can solve the Schrödinger’s equation up to the physical limit of the chosen DFT approximation and essentially we do that for a very small group of atoms and then use that to train these machine learning models,” explains Sivaraman.


Above: AI can help target likely salt combinations for further research in support of molten salt reactors 32 | March 2023 | www.neimagazine.com


Page 1  |  Page 2  |  Page 3  |  Page 4  |  Page 5  |  Page 6  |  Page 7  |  Page 8  |  Page 9  |  Page 10  |  Page 11  |  Page 12  |  Page 13  |  Page 14  |  Page 15  |  Page 16  |  Page 17  |  Page 18  |  Page 19  |  Page 20  |  Page 21  |  Page 22  |  Page 23  |  Page 24  |  Page 25  |  Page 26  |  Page 27  |  Page 28  |  Page 29  |  Page 30  |  Page 31  |  Page 32  |  Page 33  |  Page 34  |  Page 35  |  Page 36  |  Page 37  |  Page 38  |  Page 39  |  Page 40  |  Page 41  |  Page 42  |  Page 43  |  Page 44  |  Page 45