Hi Everyone,
Please see below for information from Victor about Prof. Mark Tuckerman's
visit to Harvard on Thursday, Feb. 2.
TL;DR:
- Prof. Tuckerman's theochem seminar will be 4:15p - 6:15p tomorrow at MIT
4-237 (new room)
- Prof. Tuckerman is available to talk to Aspuru-Guzik group members on
Thurs. Feb. 2nd from 11:30 - 12:15p in the Division room (please come!)
Best,
Jennifer
——
Hello,
Prof. Mark Tuckerman of NYU is visiting Harvard University on Thursday Feb
2. I have arranged a time for a meeting between the Aspuru-Guzik group and
Prof. Tuckerman between 11:30 and 12:15 in the Division room. I will make a
lunch reservation for ~12:30, so there is some margin there after 12:15.
Thus far, from the Aspuru-Guzik group, I have Nicolas and Jhonathan
confirmed for lunch and Jennifer for dinner.
Additionally, Prof. Tuckerman will be giving a seminar Wednesday 4 PM at
MIT in 4-237.
I’ve attached the full schedule for your information.
Best,
Victor
<2017-01-23__theochem_tuckerman_thu_feb2_schedule.docx>
*ABSTRACT: Exploration and learning of free energy landscapes of molecular
crystals and oligopeptides*
*Prof. Mark E. Tuckerman, New York University*
Theory, computation, and high-performance computers are playing an
increasingly important role in helping us understand, design, and
characterize a wide range of functional materials, chemical processes, and
biomolecular/biomimetic structures. The synergy of computation and
experiment is fueling a powerful approach to address some of the most
challenging scientific problems. In this talk, I will describe the efforts
we are making in my group to develop new computational methodologies that
address specific challenges in free energy exploration and generation. In
particular, I will describe our recent development of enhanced free energy
based methodologies for predicting structure and polymorphism in molecular
crystals and for determining conformational equilibria of oligopeptides.
The strategies we are pursuing include heterogeneous multiscale modeling
techniques, which allow “landmark” locations (minima and saddles) on a
high-dimensional free energy surface to be mapped out, and
temperature-accelerated methods, which allow relative free energies of the
landmarks to be generated efficiently and reliably. I will then discuss new
schemes for using machine learning techniques to represent and perform
computations using multidimensional free energy surfaces and navigate
chemical compound space in an effort to discover new compounds.
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