Dr. N. M. ANOOP KRISHNAN
Department of Civil Engineering
Yardi School of Artificial Intelligence (Joint Appt.)
Indian Institute of Technology Delhi, IN
Anoop completed his Ph.D. in Civil Engineering from Indian Institute of Science Bangalore in 2015, after which, he worked as a postdoctoral researcher in University of California Los Angeles from 2015 to 2017. Prior to this, he completed his B.Tech in Civil Engineering from National Institute of Technology Calicut in 2009. In October 2017, he joined IIT Delhi in the Department of Civil Engineering, where he is currently serving as an Associate Professor and heads the M3RG. He also holds a joint position as an Assistant Professor in the School of Artificial Intelligence, IIT Delhi. He has published more than 80 international peer-reviewed journal publications and has filed 3 patents. He has founded a start-up Substantial AI Pvt. Ltd., incubated at IIT Delhi, for AI-driven materials discovery and process optimisation. He has won several awards including Indian National Academy of Engineering Young Engineer Award (INAE YAE 2020), BRNS-DAE Young Scientist Award (2021), and National Academy of Science India Young Scientist Award (NASI YSA 2021), to name a few.
Accelerating glass modeling with machine learning and artificial intelligence
Glasses form the backbone of our society, ranging from windshields and display screens to biomedical devices and lenses. Traditional glass discovery relies on trial and error approaches thereby leading to a design to deploy period of 20-30 years. To address this challenge, in this talk, we will discuss the application of artificial intelligence (AI) and machine learning (ML) in accelerating glass modeling and discovery. Specifically, three aspects where AI and ML can be used include: (i) data-driven models for glass property predictions, (ii) natural language processing (NLP) for extracting information from the glass literature, (iii) physics-informed machine learning for glass modeling. To demonstrate these aspects, three problems will be discussed. First focuses on developing interpretable ML models for predicting 25 properties of glasses made of a few among 84 elements of the periodic table. This work covers nearly the entire periodic table for glass forming elements. Second focuses on extracting information on glasses and other materials from literature to answer specific queries. We will also discuss on MatSciBERT, the first materials-aware language model. We will also discuss how MatSciBERT can be used to extract information regarding composition-property from the glass literature. Third, we will discuss on how to accelerate simulations using physics-informed ML (PIML). Here, we will discuss how interaction laws in nature can be discovered directly from the trajectory of physical systems using PIML. Altogether, the talk will cover various aspects of AI and ML that has been used to accelerate materials discovery. Finally, a brief outlook on the future prospects will be discussed.