13-18 May 2018
Casino Conference Centre
Europe/Prague timezone

Deep learning approach for An/Ln separation problem

18 May 2018, 10:30
Red Hall (Casino Conference Centre)

Red Hall

Casino Conference Centre

Reitenbergerova 4/95, Mariánské Lázně, Czech Republic
Verbal Separation Methods, Speciation SEP 5


Artem Mitrofanov (Moscow State University)


A problem of separation of trivalent f-elements is one of key problems of spent fuel reprocessing as well as processing of lanthanide-containing mineral resources. Chemical similarity leads to development of long cascade separation equipment with low efficiency on each step. Development of new selective ligands may allow us to simplify separation scheme and increase process efficiency.
"Deep learning" is one of popular trends in science and technology nowadays. It has already proved its chemical usability in drug design area and continues spreading over other areas of chemistry.
Here we present results of development of neural net models that were trained to predict complex stability constants for a number of trivalent f-elements. While requesting a lot of time and computational resources for training, such models are easy in use and quite fast for prediction purposes. We reached determination coefficient (R^2) values up to 90~95% that allow us to determine perspective in terms of separation ligands at a design stage.

Primary authors

Artem Mitrofanov (Moscow State University) Petr Matveev (Moscow State University) Alexandru Korotcov (Science Data Software) Dr Vladimir Petrov (Moscow State University) Dr Valery Tkachenko (Science Data Software) Prof. Stepan Kalmykov (Moscow State University)

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