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

Experimental Data-driven Research on 2D layered perovskites: Integrating raw data during daily research activity

Milena Arciniegas, Istituto Italiano di Tecnologia, IIT

10:20-10:50 am

Accelerating nanoscale characterization with theory and machine learning

Maria Chan, Argonne National Laboratory

10:50-11:10 am

Dissociation of vibrationally excited nitrogen molecule on metallic surface-a neural network potential enabled molecular dynamics study

~~Abhirup Patra, University of Delaware~~

11:10-11:40 am

Open Source Molecular Machine Learning with DeepChem

Bharath Ramsundar, Deep Forest Sciences