Kimaya Mehrotra
MIT Department:
Civil and Environmental Engineering
Faculty Mentor:
Markus Buehler
Research Supervisor:
Alireza Ghafarollahi
Undergraduate Institution:
University of Illinois at Urbana-Champaign
Biography
Kimaya is a rising junior in Chemical Engineering at the University of Illinois, Urbana-Champaign. Through her research, she aims to leverage the advanced logical and analytical capabilities of computation to understand multiscale chemical systems. This summer, Kimaya is working with the Laboratory for Atomistic and Molecular Mechanics (LAMM) at MIT, designing an AI-driven closed-loop scientific discovery system for materials science. At UIUC, Kimaya has been conducting research with the Peters Lab for the past year. She has contributed to the development of the group’s Master Equation microkinetic modeling technique by designing kinetic Monte Carlo simulations to capture the bistability phenomenon in chemical systems. Kimaya values responsibility in engineering and understanding the ethical implications of emerging technologies, and hence enjoys learning about the history and philosophy of science. She is also actively committed to STEM teaching and outreach initiatives, helping young students experience the inherent wonders of science.
Sparks2D: Multiagent AI for Autonomous Discovery in 2D Materials
Kimaya Mehrotra1, Alireza Ghafarollahi2 and Markus J. Buehler2-4
1Department of Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign
2Department of Mechanical Engineering, Massachusetts Institute of Technology
3Department of Civil and Environmental Engineering, Massachusetts Institute of Technology
4Schwarzman College of Computing, Massachusetts Institute of Technology
2D materials such as graphene, first isolated in 2004 by Andre Geim and Konstantin Novoselov, have the potential to lead to revolutionary technologies in electronics, energy storage, and manufacturing due to their unique mechanical and electrical properties. However, the design space of these materials is essentially infinite, making it unfeasible for humans to search manually. Machine Learning tools are frequently used for exploration and optimization of properties in materials, but they rely heavily on curated databases with multiple known variables. General-purpose Large Language Models (LLMs) like GPT-5.6 or Fable, on the other hand, can fill in gaps in information through their broad knowledge bases. Hence, autonomous AI frameworks, when combined with evolutionary algorithms and first-principles calculations, can generate creative structures with potentially useful properties. Here, we present Sparks2D, an autonomous scientific discovery system, to uncover mechanistic principles in graphene. To test a proposed hypothesis, AI agents propose different designs of modified graphene, which are evaluated through physics-based simulations. Sparks2D then iterated upon designs through a “survival of the fittest” evolutionary process, based on a scoring system that prioritizes optimization of target material properties. Through this, I aim to enhance the self-directed innovation of Sparks2D for inverse design of materials.
