Generative machine learning models to predict novel stable materials

Data-driven machine learning models are increasingly being used to generate stable materials which form the basis for rational design of novel catalysts and battery materials. These models are trained on a database of stable materials computed using first principles methods. Based on this database, architectures such as the variational auto-encoder, generative adversarial networks and (more recently) the transformer learn an implicit probability distribution. This distribution is used to decides if a given material is stable. 

Tracking Protein Chaos in Neurodegeneration Simulations

Intrinsically disordered proteins (IDPs) are special proteins without a fixed 3D shape, like wiggly strings that flex to do key jobs in cells, but in diseases like Parkinson's (alpha-synuclein) or Alzheimer's (tau), mutations make them clump into toxic tangles harming brain cells. This project uses molecular dynamics (MD) simulations—computer models that mimic atomic wiggles over time—to watch how these changes spread along the protein chain, spotting "switches" that trigger clumping.

Computer Simulations to Unravel Protein Clumps Causing Type 2 Diabetes

Intrinsically disordered proteins are proteins that lack a stable three dimensional structure. In many cases they control vital cellular processes or have been associated with disease. In this project, we use computer simulations to study a protein called IAPP that clumps up harmfully in type 2 diabetes, killing the cells that make insulin. Lacking fixed structure, IAPP's flexible conformations shift with mutations toward fibril-prone states that block insulin from working properly.

How do cells react to microplastic pollution?

BTP
Developing an understanding the effect of microplastics on cellular function is important. This project will involve preparing microplastics from common commercial polymers (such as polyethylene, polypropylene, polystyrene and polyesters) and their thorough characterization. One aspect of this project will be to come up with a protocol to generate fluorescently tagged microplastics to allow them to be tracked as they are ingested by cells. Subsequently, cells will be exposed to these microplastics and their response will be studied.

Innovative Wormlike Micelle Simulation Model Earns Journal Cover Spotlight

Congratulations to Avishek Kumar, PhD student at the IITB–Monash Research Academy, supervised by Prof. P. Sunthar (IIT Bombay) and Prof. J. Ravi Prakash (Monash University). His groundbreaking research introducing the persistent-worm model for the mesoscopic simulation of wormlike micelles (WLMs) has been featured on the cover of the Journal of Rheology.

IIT Bombay Identifies Biomarkers To Predict Kidney Failure in Diabetics

Research led by the Indian Institute of Technology Bombay has identified hidden blood biomarkers that can flag early warning signs of diabetic kidney disease.

The finding, according to researchers, could help in early diagnosis and pave the way for more personalised treatment.

India, often called the diabetes capital of the world, has around 101 million adults currently living with the condition and another 136 million at risk with prediabetes, making Type 2 diabetes a public health concern in the country.

Conference on Colloids and Surfactants in Industrial Applications: Fundamental Principles and Advances

The Indian Institute of Technology, Bombay, is organizing a Conference on Colloids and Surfactants in Industrial Applications: Fundamental Principles and Advances from 14th to 16th November 2025 at the Victor Menezes Convention Centre (VMCC), IIT Bombay.

This conference aims to bring together researchers, academicians, and industry professionals to discuss recent developments, emerging trends, and innovative applications of colloids and surfactants across various industrial sectors.

Subscribe to