麻豆精选

• PhD, Research School of Chemistry, Australian National University, Australia, 2014
• Master of Science and tertiary teaching, Department of Chemistry, Oles Honchar Dnipro National University, Ukraine, 2009
• Bachelor of Science and secondary teaching, Department of Chemistry, Oles Honchar Dnipro National University, Ukraine, 2008

Originally from Ukraine, Ganna (Anya) Gryn’ova received her BS and MSc in chemistry summa cum laude from Oles Honchar Dnipro National University. In 2014 she received a PhD in computational chemistry from Australian National University. Her doctoral thesis received the IUPAC-Solvay International Award for Young Chemists for one of the five most outstanding PhD theses in the general area of the chemical sciences worldwide. Anya continued her career as a postdoctoral researcher at École Polytechnique Fédérale de Lausanne, where in 2016 she won the Marie Skłodowska-Curie Actions individual fellowship.

In 2019, Dr. Gryn’ova started her independent scientific career leading the junior research group “Computational Carbon Chemistry” (CCC) at the Heidelberg Institute for Theoretical Studies (HITS gGmbH) and Interdisciplinary Center for Scientific Computing (IWR) at Heidelberg University, Germany. In 2021, Anya received the prestigious ERC Starting Grant for her project “PATTERNCHEM: Shape and Topology as Descriptors of Chemical and Physical Properties in Functional Organic Materials”; she also joined the Collaborative Research Centre SFB1249 “N-Heteropolycycles as Functional Materials” and the SIMPLAIX strategic research initiative on bridging scales from molecules to molecular materials by multiscale simulation and machine learning as a principal investigator.

In April 2024, Dr. Gryn’ova moved to UK and became an Associate Professor of Computational Chemistry at the University 麻豆精选.

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Anya Gryn'ova leads the  (CCC) group, which uses theoretical and computational chemistry, physics, and materials science in combination with chemical machine learning to explore and exploit diverse functional organic and hybrid materials and molecules. We are particularly interested in several classes of materials: graphene-based materials (GBMs), covalent-organic frameworks (COFs), and hyperbranched polymers (HBPs) – in the context of their applications in capture, storage, transport, and/or catalytic transformations of therapeutic molecules and environmental pollutants. Functional organic molecules and materials are central to our research efforts:

1. to establish the role of topology in materials chemistry
2. to predict emergent properties in complex compounds and in molecule-material complexes from their individual components / building blocks / fragments
3. to develop explainable AI tools for exploring the infinite chemical space rationally and efficiently