Abstract

In this presentation, I will describe how data science, machine learning (ML) and high-throughput synthesis can be combined to achieve accelerated development and discovery of solid-state materials for energy applications. I will describe 6S, a self-sintered scalable solid-state synthesis technique and show its application in the realm of thermoelectric materials and metal alloys for thermal catalysis. Lastly, I will showcase some examples of using large language models (LLMs) as tools for education, and how to expand the framework to scientific research.

Bio:

Jose Recatalà Gómez is a research fellow in the Materials Science and Engineering Department at Nanyang Technological University (NTU), Singapore, working under Associate Professor Kedar Hippalgaonkar. He specializes in integrating Generative AI and machine learning with automated solid-state synthesis for discovering inorganic materials for energy and environmental applications. Jose earned his Bachelor’s in Chemistry from Universitat Jaume I, Spain, in 2015, a Master’s in Advanced Materials from Universidad Autónoma de Madrid, Spain, in 2016, and a PhD from the University of Southampton, England, in 2021. He was also an A*STAR Research Attachment Programme (ARAP) scholarship recipient, spending two years at the Institute of Materials Research and Engineering (IMRE) in Singapore.