Hi all,
Due to the tight schedule, and large amount of uptake from faculty, there
are only two half hour slots for meetings, which have gone to the Samsung
project (Rafa, Jorge, Jennifer and Tim) and Stephanie. There will,
however, be a 1h group session 4:30-5:30 in the common area, where you can
ask your burning questions.
Ed
On 2 April 2014 14:09, Edward Pyzer-Knapp <e.o.pyzerknapp(a)gmail.com> wrote:
Hi all.
Professor Todd Martinez from Stanford will be visiting the Boston area to
give a talk as part of the shared Harvard/MIT/BU theoretical chemistry
seminar series.The title of his presentation is Leveraging Machine
Learning and
Stream Processors for Theoretical Chemistry(please find his abstract
below).
Please tell me if you are interested in talking with him, since I have a
few slots in the afternoon left. I also am looking for people to lunch
with him as well (first come first served) (8th April)
The seminar is scheduled on the next day, April 9th, at 4:00 PM in room
4-163 (MIT).
Ed Pyzer-Knapp
*************************************************************************************
Leveraging Machine Learning and Stream Processors for Theoretical Chemistry
Todd J. Martínez
Department of Chemistry, Stanford University, Stanford, CA 94305
Abstract: Novel computational architectures and methodologies are
revolutionizing
diverse areas ranging from video gaming to advertising and espionage. In
this talk, I
will discuss how these tools and ideas can be exploited in the context of
theoretical and
computational chemistry. I will discuss how insights gleaned from
recommendation
systems (such as those used by Netflix and Amazon) can lead to reduced
scaling methods
for electronic structure, how the algorithms in electronic structure can
be adapted for
commodity stream processing architectures such as graphical processing
units, and how
nonlinear dimensionality reduction methods can be used to extract chemical
knowledge
from the resulting data. I will discuss some of the details regarding the
structure and
implementation of these methods. I will also show how these advances can
be harnessed to
progress from traditional "hypothesis-driven" methods for using electronic
structure and
first principles molecular dynamics to a "discovery-driven" mode where the
computer is
tasked with discovering chemical reaction networks. Finally, I will show
how these can be
combined with force-feedback (haptic) input devices and three-dimensional
visualization
to create molecular model kits that carry complete information about the
underlying
electrons. This interactive first principles molecular dynamics method
(molecular
computer-aided design or mol-CAD) opens the door to novel ways of teaching
chemistry
and may also be of use in applied chemical research.