Date/Time Date(s) - 13/02/20262:30 pm - 3:30 pm
Categories
Prof. Anton Oliynyk
Hunter College, City University of New York
Machine learning (ML) successfully predicts crystal structures with supervised methods, where training data has the structure type specified. For the last decade, the datasets of equiatomic compounds, Heusler, perovskites, and spinels have been used to test novel supervised algorithms and approaches. However, unsupervised ML for materials prediction is underexplored. To demonstrate the potential of unsupervised learning methods, a novel PuNi3-type phase, TbIr3, was predicted and synthesized, revealing how exploratory chemical decisions could be guided through ML.1 We developed software to extract useful descriptors from crystallographic information in a high-throughput way to make them ready for unsupervised learning, where machine needs to find out the differences between the structures on its own and predict the number of structure clusters. Our software focuses on explainable machine learning, with which we gain chemical insights in contrast to common black-box machine-learning methods. To expand the functionality of materials informatics tools, we automated traditional exploratory synthesis strategies with a recommendation engine that visualizes crystal structure trends and proposes the next best element to try when considering exploratory synthesis. Building up on the ML discovery success, we detail the discovery of a novel Gd–Ru–Cd phase, which is an excellent neutron absorber and a material with a negative thermal expansion.2
Anton Oliynyk completed his PhD at University of Alberta, postdoctoral studies at University of Houston, and continued as a research associate again at University Alberta. As an independent researcher, he was an assistant professor at Manhattan University, and later continued at Hunter College, City University of New York.
Anton Oliynyk has 16 years of experience in solid state chemistry synthesis and X-ray crystallography, complemented with 13 years of experience in machine learning. Combining machine learning with experimental work, he synthesized over 480 novel intermetallic phases as exploratory projects or as an experimental validation for machine-learning models.
Currently, Anton Oliynyk is working in the area of solid state radiochemistry, with a focus on intermetallic materials for nuclear applications.
In-Person: ABB 102
Online: Echo360