Ciclo de Seminarios 2019
2019 10 JUL
Miércoles 10 de Julio
11:30 hs. - Auditorio Emma Pérez Ferreira
Edificio TANDAR
"Machine learning and molecular dynamics"
Prof. Michele Parrinello
(*)
Department of Chemistry and Applied Biosciences, ETH Zürich
Università della Svizzera italiana, Lugano, Switzerland
RESUMEN:
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.
* Michele Parrinello is currently Professor at ETH Zurich, and the Università della Svizzera italiana Lugano, Switzerland. He is known for his many technical innovations in the field of atomistic simulations and for a wealth of interdisciplinary applications ranging from materials science to chemistry and biology. For his work he has been awarded the 2011 Prix Benoist, the 2017 Dreyfus Prize and many others prizes and honorary degrees. He is a member of numerous academies and learned societies, including the National Academy of Science, the British Royal Society and the Italian Accademia Nazionale dei Lincei. He is the author of more than 600 papers and his work is highly cited.