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  • Katherine Hollingsworth

UGM Webinar: Machine Learning and Molecular Dynamics


Upcoming Webinar

Presented by Professor Michele Parrinello

Join Professor Michele Parrinello in the next UGM webinar session:


Atomistic simulations based on an atomistic description of matter have become a most important scientific tool of contemporary material science investigations. Yet much remains to be done to extend the scope of these simulations.

Very often, the quality of the models used falls short of what would be needed; the size of the system that can be simulated is too small or the-time scales that can be simulated much too short to be of relevance.

In this talk, we show how modern machine learning techniques, working in a synergetic way with molecular dynamics simulation, offer a way of overcoming these problems to bridge the gap between simulation and real life experiments.

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Live Webinar Dates

Thursday, October 28th: 08:00 PDT (USA) 11:00 EDT (USA) 16:00 GMT (EUROPE) 17:00 CEST (EUROPE) 20:30 IST (INDIA) Join us after the presentation for a live Q&A session.


Other internationally renowned speakers at the User Group Meeting include:

  • Professor Sir Richard Catlow FRS (University College London, England)

  • Professor Georg Kresse (University of Vienna, Austria)

  • Professor Chris Van de Walle (University of California Santa Barbara, USA)​


Professor Michele Parrinello Michele Parrinello is currently principal investigator at the Italian Institute of Technology in Genoa, Italy.

Together with Roberto Car he has introduced the ab-initio molecular dynamics method. This method, which goes under the name of Car-Parrinello Method, represents the beginning of a new field, and has dramatically influenced both molecular dynamics simulations and electronic structure calculations.

He is also known for the Parrinello-Rahman method of molecular dynamics, which permits the study of crystalline phase transitions under constant stress. In the last twenty years, he has developed metadynamics, and other enhanced sampling methods for the study of rare events and the calculation of free energies.

He has also pioneered the application machine learning methods to molecular dynamics simulation an area of research that is now booming and to which he is actively contributing.

Parrinello’s scientific interests are strongly interdisciplinary and include the study of complex chemical reactions, materials, and protein dynamics. The methods developed by Parrinello have greatly extended the scope of atomistic simulations. The more than one hundred thousand citations that his work has received bear witness to the impact of Parrinello’s work.

His contributions have made their way into university textbook. He has educated hundreds of students and post-docs many of which now occupy important positions both in academe and in industry.




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