Invitation to ICTP-EAIFR Webinar Colloquium by Prof. Michele Parrinello on "Machine Learning and Molecular Dynamics" on Thursday 22 October 2020 at 16:00 CET

[ICTP/director]

Dear All,

EAIFR, ICTP's partner Institute in Rwanda, is organizing a Webinar 
Colloquium by Prof. Michele Parrinello on "Machine Learning and 
Molecular Dynamics", on Thursday 22 October, at 16:00 hrs CET

Register in advance for this webinar:

https://zoom.us/webinar/register/WN_rvxagvQTQ8-FseVUPqOo9w

After registering, you will receive a confirmation email containing 
information about joining the webinar.

The colloquium will also be streamed at: https://ictp.it/livestream

Biosketch: Michele Parrinello is Professor in Computational Sciences, 
ETH, Zurichand Università della Svizzera Italiana USI, Lugano, 
Switzerland, 2001-present.

He  is the world's leading expert in atomistic/molecular simulations. He 
obtained his degree in physics in 1968 from Bologna, Italy. Prior to 
joining ETH, he was Director at the Max Planck Institute for Solid State 
Research in Stuttgart, Germany, and previous positions include research 
staff member at the IBM Research Laboratory in Zurich, Switzerland, and 
full professor at SISSA, Trieste, Italy. He is member of the ICTP 
Scientific Council.

Professor Parrinello'​s scientific interests are strongly 
interdisciplinary and include the study of complex chemical reactions, 
hydrogen-​bonded systems, catalysis and materials science. Together with 
Roberto Car he introduced the ab-​initio molecular dynamics method, 
which he is still developing and applying. This method, which goes under 
the name of Car-​Parrinello method, represents the beginning of a new 
field and has dramatically influenced the field of electronic structure 
calculations for solids, liquids and molecules. He is also known for the 
Parrinello-​Rahman method of molecular dynamics, which permits the study 
of crystalline phase transitions under constant pressure. For his 
research he has been awarded numerous prizes, including the 2001 Award 
in Theoretical Chemistry of the American Chemical Society, the 1995 
Rahman prize of the American Physical Society and the 1990 
Hewlett-​Packard Europhysics prize. He is an External Scientific Member 
of the Max Planck Institute for Solid State Research, a Fellow of the 
American Physical Society and a Member of the International Academy of 
Quantum Molecular Science and of the Berlin-​Brandenburgische Akademie 
der Wissenschaften.

Abstract:

Atom based computer simulation is one of the most important tools of 
contemporary physical chemistry. In spite of its many successes, it 
suffers from severe limitations. Here we show how machine-learning 
techniques can help in solving at least two different problems. The 
first one is the accuracy of current interatomic potential models; the 
second is the limited time scale that simulations can explore. In order 
to solve the first problem we train a neural network on a set of 
accurate but expensive quantum chemical calculations. In this way, it is 
possible to obtain an accurate description of the system at a relatively 
low computational cost. Crucial for the success of this program has been 
the design of the neural work and the selection of the training set. We 
apply this approach to study a metal non-metal transition and to 
chemical reactions in condensed phases. These applications would not 
have been possible without the use of efficient sampling methods capable 
of lifting the time scale barrier. To this effect, we have developed two 
very efficient sampling methods, metadynamics and variationally enhanced 
sampling. Both methods are based on the identification of appropriate 
collective variables, or slow modes, whose sampling needs to be 
accelerated. Machine learning can be used also for the construction of 
efficient collective variables based on a modification of the well-known 
linear discriminant analysis classification method. Finally, we use the 
variational enhanced sampling approach and a deep neural network to 
further increase our sampling ability.

We look forward to seeing you on-line.

Best regards,

Director's Office, ICTP