Role of Computations, Data and AI in Materials and Molecule Design: An Industrial Perspective (07/11/23)

Speaker and Affliation:

Dr. Sharankumar Shetty
Shell Technology Centre, Bangalore


07th November, 2023 (Tuesday), 3.00 PM (India Standard Time)


K I Vasu Auditorium, Dept. of Materials Engineering, IISc, Bangalore

Bio Data

Sharan is a Principal Scientist in Computational Science team at Shell Technology Centre, Bangalore. Sharan did his Ph. D. from National chemical Laboratory, India under the guidance of Dr. Sourav Pal (NCL) and Prof. D. G. Kanhere (Univ. of Pune) in the field of computational chemistry. Later, he moved to the University of Eindhoven (TU/e) to pursue his post-doc in the group of Prof. Rutger van Santen, where he worked on the applications of computational chemistry in the field of Fischer-Tropsch synthesis, syngas to oxygenates and ammonia synthesis. After his post-doc, he joined SABIC Technology Centre, Bangalore where he worked on projects involving engineering plastics, polyolefin catalyst development, olefin crackers and advanced material. He joined Shell in 2018 and has been involved in several computational projects on catalysis, material science and data-domain analytics. He has more than 40 publications in high impact factor journals and 6 patents filed/granted.


In recent years there has been an incredible growth towards the design of advanced materials in the field of battery, carbon capture and utilization, energy storage, polymers, catalysis etc mainly focusing on decarbonizing the industrial processes to achieve sustainable targets. Synthesis of low-cost and scalable materials is needed for large scale deployment in the industry. However, the discovery of novel materials and molecules are constrained by the underlying chemical complexity of the structure-property relationship.
Computational materials science has become a guiding principle to provide insight into the design of materials and molecules encompassing the atomic scale to the meso-scale simulations. These methods in conjunction with experimental inputs has accelerated the discovery of new materials at low cost and time. In the last decade, materials databases that have been generated from the conventional methods has helped in advancing the computational materials research where machine learning tools have been successfully employed for accelerated materials informatics at an accelerated pace. In this presentation, I will discuss the role of conventional computational methods and Machine Learning approaches for providing insight into the materials and molecular design for industrial application such as CO2 capture and utilization, corrosion, catalysis and battery. I will also discuss the challenges and opportunities of materials application for the industrial processes.