Data-driven and Machine-learning Approaches to Nanoscale and Molecular Systems

Session 2A

Arkadeep Kumar, Shruba Gangopadhyay, Steve Whitelam

This symposium will focus on data-driven and machine-learning approaches to materials science. Such approaches, which range from the development of materials databases to the application of reinforcement learning to time-dependent protocols, promise new ways of discovering materials, designing devices, and controlling experiments. Our symposium will bring together scientists from academia and industry who work in these areas or are interested in them. 

Session Schedule:

(abstracts below)

10:00-10:20 am

Milena Arciniegas, Istituto Italiano di Tecnologia, IIT

10:20-10:50 am

Maria Chan, Argonne National Laboratory

10:50-11:10 am

Abhirup Patra, University of Delaware

11:10-11:40 am

Bharath Ramsundar, Deep Forest Sciences

11:40-12:00 pm

Xingzhi Wang, UC Berkeley

12:00-12:30 pm

Dmitry Zubarev, IBM Almaden Research Center

Abstracts