Simulating lanthanide and actinide chemistry through accurate computational methodologies is important to better understand binding selectivity of lanthanides and actinides in nuclear and radiochemical applications. This is crucial for nuclear forensics, designing separation agents, and understanding spectra. Theoretical predictions using molecular modeling methods, like ab initio and DFT, have been popular to simulate binding interactions. Using modern data modeling methods, like Artificial Intelligence techniques, such as machine and deep learning, can be insightful to understanding binding selectivity properties. Findings from recent studies with machine and deep learning applications in separations chemistry will be presented.

Applying artificial intelligence methods to develop computational protocols for lanthanide and actinide systems

ACS Spring 2022

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