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Machine Learning in Chemistry

 
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About this Course

About the Course

In this course Professor Mark Tuckerman (New York University) introduces us to the concept of machine learning, a relatively new approach in computer science to solving complex problems. Machine learning has already proved to have widespread applications in industry and science, but in this lecture series Professor Tuckerman the applications of machine learning to problems in the field of chemistry. We begin by (i) introducing the term ‘machine learning’, everyday uses in computing and in the history of science, as well as its origin; (ii) followed by discussing where machine learning can be applied in chemistry, the problems that face the chemical industry as well as its academia; (iii) then moving to discuss molecular dynamics, the physics that govern molecules, and the ways in which machine learning can save hugely on computing costs in these areas; (iv) and finally investigating more closely and specifically the progress of modern research optimising machine learning for purposes in chemistry.

About the Lecturer

Mark Tuckerman obtained his B.S. in physics from the University of California at Berkeley in 1986 and his Ph.D. from Columbia University in 1993, working in the group of Bruce J. Berne. From 1993-1994, he held an IBM postdoctoral fellowship at the IBM Forschungslaboratorium in Rüschlikon, Switzerland in the computational physics group of Michele Parrinello. From 1995-1996, he held an NSF postdoctoral fellowship in Advanced Scientific Computing at the University of Pennsylvania in the group of Michael L. Klein. He is currently Professor of Chemistry and Mathematics at New York University. His research interests include the use of theoretical and computational chemistry techniques to study proton and hydroxide transport in bulk and confined hydrogen-bonded liquids and hydrogen diffusion in nanoconfined materials, the development of large time-step molecular dynamics and machine learning algorithms and free-energy based enhanced sampling tools for predicting the conformational equilibria of complex molecules and the exploration of structure and polymorphism in molecular crystals, and development of machine learning models in density functional theory and statistical mechanics. Honors and awards include the Japan Society for the Promotion of Science Fellowship, the Friedrich Wilhelm Bessel Research Award from the Alexander von Humboldt Foundation, the Camille Dreyfus Teacher-Scholar Award, an NSF CAREER Award, and the NYU Golden Dozen Teaching Excellence Award, the Sentinels of Science Award from Publons, the Andreas C. Albrecht Lectureship from Cornell University, and the Institute Lectureship from the Indian Institute of Technology, Kanpur.

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