Division/Committee: [ENFL: Division of Energy and Fuels]

This symposium, in response to demanding ambitious global decarbonization goals, aims to highlight approaches which accelerate the discovery and development of materials vital for energy storage, energy-efficient processes, and other carbon emissions reducing technologies. Despite advancements in autonomous materials discovery, numerous challenges persist, such as acquiring high-quality datasets, refining, and developing predictive models, hardware and software integration for autonomous experimentation, high volume data management and autonomous analysis. This symposium aims to facilitate interdisciplinary collaboration by bringing together materials scientists, computational experts, and automation specialists to explore the fusion of their respective fields in accelerating materials development. Due to the emerging nature of these approaches, our goal is that sharing best practices among practitioners on the cutting edge will help to overcome current limitations in energy technology and advocate for the transition to a sustainable future of energy systems. Two sessions are planned: (1) autonomous laboratory (2) Computational discovery

Steven B. Torrisi

Graduate Student, Harvard University

Appearances