J.C. Bose Memorial Lecture 2017

 

Speaker : Professor Todd Martinez
Stanford University
Topic : “Discovering Chemistry With Advanced Computing and Machine Learning”
Date : Tuesday, 07th February, 2017
Time : 11:30 AM
Venue : C.V. Raman hall, IACS

 

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[1] (solving the electronic Schrodinger equation to describe molecules) and how the algorithms in electronic structure can be adapted for commodity stream processing architectures such as graphical processing units.[2] I will 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.[3] 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 mCAD) opens the door to novel ways of teaching chemistry and may also be of use in applied chemical research.[4]

[1] C. Song and T. J. Martínez, J. Chem. Phys. 146, 034104 (2017)
[2] F. Liu, N. Luehr, H. J. Kulik, and T. J. Martínez, J. Chem. Theo. Comp. 11, 3131 (2015)
[3] L.-P. Wang, A. Titov, R. McGibbon, F. Liu, V. S. Pande and T. J. Martínez, Nature Chem. 6, 1044-1048 (2014)
[4] N. Luehr, A. G. B. Jin and T. J. Martínez, J. Chem. Theo. Comp. 11, 4536 (2015)

 

 

                 All are cordially invited to attend