Rishi Gurnani

Rishi Gurnani

Technology Development Lead at Matmerize

Rishi Gurnani is the Director of Software Engineering at Matmerize, the new standard for accelerating polymeric materials development, powered by machine learning and AI. 
Rishi earned his PhD at Georgia Tech where he used artificial intelligence to design materials for energy storage. He continues to do fundamental research in this area and has authored a number of peer-reviewed publications and has been invited to speak at several conferences. Previously, he received his Bachelor of Science in Materials Science and Engineering from the University of California, Berkeley.  

ublications (in chronological order): 

Effect of Fluorine in Redesigning Energy-Storage Properties of High-Temperature Dielectric Polymers, A.A. Deshmukh, C. Wu, O. Yassin, L. Chen, S. Shukla, J. Zhou, A.R. Khomane, R. Gurnani, T. Lei, X. Liang, R. Ramprasad, Y. Cao, G. Sotzing, ACS Applied Materials & Interfaces (2023).
Polymer informatics at-scale with multitask graph neural networks, R. Gurnani, C. Kuenneth, A. Toland and R. Ramprasad, Chemistry of Materials, (2023).
Identifying High-Performance Metal−Organic Frameworks for Low-Temperature Oxygen Recovery from Helium by Computational Screening, S. Jamdade, R. Gurnani, H. Fang, S. Boulfelfel, R. Ramprasad, and D.S. Sholl, Industrial & Engineering Chemistry Research, (2023).
polyG2G: A Novel Machine Learning Algorithm Applied to the Generative Design of Polymer Dielectrics, R. Gurnani, D. Kamal, H. Tran, H. Sahu, K. Scharm, U. Ashraf and R. Ramprasad, Chemistry of Materials, (2021).
Interpretable Machine Learning-Based Predictions of Methane Uptake Isotherms in Metal–Organic Frameworks, R. Gurnani, Z. Yu, C.Kim, D.S. Sholl, and R. Ramprasad, Chemistry of Materials, (2021).

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