Akshat Zalte
Akshat Zalte is a PhD student in chemical engineering whose research involves innovative molecular representations for application to machine learning in chemistry. His research also spans process modeling and techno-economic analysis of future fuel systems to decarbonize long-haul trucking. As a MathWorks Fellow, Akshat will pursue research in two primary areas: making key improvements to Chemprop and evaluating various liquid energy carriers for their potential value in long-haul trucking. He plans to create a model that can learn based solely on the connections within a given molecule without knowledge of bond order and integrate optical and geometrical isomerism to create 2.5D approaches capturing chirality without the need for full 3D structural data. He will also expand an existing MATLAB framework to assess the economic and emissions implications of vehicle technologies and liquid fuel options such as Fischer-Tropsch diesel and liquid organic hydrogen carriers. Akshat’s work has the potential to yield discoveries that will accelerate the decarbonization of long-haul transportation, as well as provide elegant tools to help researchers in cheminformatics test and hone machine-learning architectures.