Title: End-to-end AI Framework for Interpretable Prediction of Molecular and Crystal Properties
Bio: I am a 5th year PhD candidate at Professor Emad Tajkhorshid’s lab in Biophysics and Quantitative Biology program at University of Illinois at Urbana-Champaign. At Argonne National Lab, I work with Dr. Eliu Huerta on applications at the interface of AI and supercomputing for physics and biophysics. My main research interests are developing and applying machine learning (ML) methods to molecular structures. I have applied ML methods to proteins, polymers and metal-organic frameworks (MOF). I like playing tennis during spare time.
Abstract:
In this talk, I will target audience who are interested in understanding the basics of how to train, perform hyperparameter search and infer an ML model for molecular structures such as small molecules or MOFs. Moreover, with a bit of Python programming knowledge, my AI framework can help users who want to learn how to visualize learned molecular representations and highlight atoms important for prediction. I also demonstrate how ML potential can be used to perform molecular dynamics (MD). Overall, the audience can expect to learn comprehensive ML techniques developed and applied for molecular studies.
Colab:
https://colab.research.google.com/drive/1viGlpUIGmRW-kzqERj4fGzX9udEXVrXw?usp=sharing
