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L’EPFL étant au cœur des défis de l’enseignement, de la recherche et du transfert de technologie, la vie sur le campus reflète cette particularité en offrant un lieu exceptionnel où se rencontrent plus de 125 nationalités.

Exploring Chemical Space with Machine Learning

Chemical (molecular, quantum) machine learning relies on representing molecules in unique and informative ways. Here, we introduce two new representations – a quantum-inspired representation called matrix of orthogonalised…


Avec: 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 gathered a number of awards, including 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. Dr. Gryn’ova continued her research career at École Polytechnique Fédérale de Lausanne as a postdoctoral researcher working on in silico modeling of organic semiconductors. In 2016 she won the Marie Skłodowska-Curie Actions individual fellowship and focussed on the non-conventional architectures of single-molecule junctions. 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. The CCC group uses state-of-the-art computational chemistry and data science to explore and exploit diverse functional organic materials for applications in organocatalysis and environmental remediation. 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 is also a principal investigator in 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.
Lieu et salle: BCH 2218
En ligne: https://epfl.zoom.us/j/68447908297?pwd=OU5JUGJUSUhZc0ZNYjQ2WENvYlNRdz09
Catégorie: Conférences – Séminaires