As a researcher specializing in the chemistry of heavy elements, I face the unique challenge of studying isotopes that decay almost instantly and can only be produced one atom at a time. My work aims to optimize the experimental design for these elements by constructing machine learning models to simulate their behavior in complex extraction systems.
During my MANABIYA internship at ICReDD, I expanded my expertise from traditional experimental chemistry into the realm of chemoinformatics. Under the supervision of Prof. Pavel Sidorov, I focused on data pre-processing and application of regression algorithms to predict distribution ratios in liquid-liquid extraction. One of highlights of my stay was incorporating quantum chemical descriptors, obtained thanks to the computational resources of ICReDD MANABIYA server, into the features for machine learning.
The collaborative environment at ICReDD provided a profound shift in perspective from my home institution. I greatly valued the opportunity to be surrounded by an international community and to interact daily with senior colleagues and researchers with extensive expertise. This experience has been valuable in shaping my research approach, providing me with both the technical toolkit and the professional inspiration to pursue innovative solutions in the field of radiochemistry.
Enni Khult
PhD student, The University of Osaka, Japan