Professor Anton Oliynyk earned his PhD in Chemistry from the University of Alberta in 2016. His primary focus is on metals and metalloids, including uranium, thorium, and rare-earth intermetallics.
PhD Chemistry, University of Alberta, Edmonton, Canada
MSc and BSc Chemistry, Ivan Franko National University of Lviv, Ukraine
Solid state chemistry
Machine-learning predictions in chemistry
Working on intermetallics (compounds of metals and metalloids), Anton Oliynyk synthesizes and characterizes novel compounds and their crystal structures. Synthesis involves high-temperature methods such as sintering, arc-melting, and metal flux growth. When new compounds form, powder and single crystal diffraction help us to study the structure and scanning electron microscope with energy dispersive spectroscopy confirms the composition of a novel compound.
The compounds are studied further with electronic structure calculations to learn more about the chemical bonding in our compounds. The characterization continues with mechanical property studies: compressive strength and hardness measurements, along with resonant ultrasound spectroscopy.
A specific focus in Oliynyk's research is on uranium, thorium, and rare-earth intermetallics.
To guide the exploratory synthesis, Oliynyk uses machine-learning approaches to predict and systematize crystal structures of solids with classification. With regression, professor Oliynyk predicts the physical properties of solids. Every machine-learning model he publishes is experimentally validated.
AO Oliynyk, E Antono, TD Sparks, L Ghadbeigi, MW Gaultois, B Meredig, ...High-throughput machine-learning-driven synthesis of full-Heusler compounds Chemistry of Materials 28 (20), 7324-7331 310 2016
A Mansouri Tehrani, AO Oliynyk, M Parry, Z Rizvi, S Couper, F Lin, ...Machine learning directed search for ultraincompressible, superhard materials Journal of the American Chemical Society 140 (31), 9844-9853 248 2018
MW Gaultois, AO Oliynyk, A Mar, TD Sparks, GJ Mulholland, B Meredig Perspective: Web-based machine learning models for real-time screening of thermoelectric materials properties Apl Materials 4 (5) 168 2016
AO Oliynyk, A Mar Discovery of intermetallic compounds from traditional to machine-learning approaches Accounts of chemical research 51 (1), 59-68 108 2018
AO Oliynyk, LA Adutwum, BW Rudyk, H Pisavadia, S Lotfi, V Hlukhyy, ...Disentangling Structural Confusion through Machine Learning: Structure Prediction and Polymorphism of Equiatomic Ternary Phases ABC Journal of the American Chemical Society 139 (49), 17870-17881 91 2017