Jaseela Madappat

What inspired you to pursue this project and join the DAMC CDT?  

I have always been fascinated by developing computational tools to solve complex problems. During my Bachelor’s, I started to explore how I could apply my coding skills to chemistry to make a meaningful impact. Back then, I was unaware of the vast field of digital chemistry, but this curiosity led me to create Inorganic Lab Analyst, a mini-project designed to help experimental chemists with salt analysis.

After completing my Master’s in Chemistry, I wanted to push my skills further, tackle open-ended problems, and develop rigorous approaches to understand complex systems. While I began working with fundamental theory to build a computational framework, I realized that I was most excited by application-oriented work, where my research could translate directly into tangible results.

When I found the DAMC CDT project on developing predictive molecular models of high-performance elastomers, I immediately saw it as the perfect opportunity. The CDT's collaborative training, its integration of fundamental research with industrial challenges, and its focus on transferable skills were exactly what I was looking for, as the program combined my interests in computation and chemistry with real-world impact.

What is your research project about, and what impact do you hope it will have?     

My project aims to develop models to predict the evolution of the storage and loss moduli of high-performance rubbers under large and persistent thermomechanical loads. Achieving this challenge requires linking changes across multiple temporospatial scales, from molecular structure and kinetics to macroscopic material properties, using both experimental data and computational tools.  Elastomers owe their widespread applications to their viscoelastic nature, which allows them to recover after significant deformation. In demanding applications such as automotive and aerospace, they must withstand fluctuating temperatures and repeated deformations. Direct testing under such conditions is expensive and often impractical, creating a need for reliable models that can simulate performance and anticipate failure. The broader impact of my project lies in advancing the design of durable elastomers for extreme environments. By reducing reliance on extensive testing, predictive modelling can accelerate material development, improve performance, and support more sustainable solutions in sectors such as automotive and aerospace.

What has been the most exciting or rewarding part of your PhD journey so far? How does your project benefit from being part of an interdisciplinary CDT like DAMC?  

The most rewarding part of my PhD so far has been learning to break a complex problem into modules, enabling me to approach to tackle it systematically. I have also been excited to see how theory translates into meaningful computational results.  Being part of the DAMC CDT adds significant value through its interdisciplinary network, supporting environment, and training opportunities, which broaden my perspective and ensure my project remains scientifically rigorous and aligned with real-world materials challenges.