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Ciclo de Seminarios 2019

Un destacado profesional invitado expone acerca de cuestiones de interés para la comunidad científica, no necesariamente vinculados directamente a temas de física. Una actividad que cuenta con más de 50 años de historia. La exposición tiene una duración aproximada de 45 minutos y a su término, la sesión queda abierta a las preguntas y los comentarios del auditorio. Tienen lugar cada dos semanas, los jueves a las 11:00 hrs. en el Auditorio "Emma Pérez Ferreira" del Edificio TANDAR, a menos que haya una indicación en contrario. Los asistentes son convidados con un café 15 minutos antes de la exposición.

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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.
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