From Chaos to Clarity: Autonomous Materials Discovery for Extreme Environments

Research output: NRELPresentation

Abstract

The pursuit of advanced functional materials for energy applications demands an understanding of their behavior under the most challenging conditions. Extreme environments, characterized by intense radiation, high temperatures, and corrosive chemistries, push materials to their limits, often revealing unexpected behaviors and degradation pathways. Traditional materials research approaches, relying on trial-and-error experimentation, are often slow and resource-intensive, ill-suited to the complexities of extreme environments. This talk will explore the transformative potential of autonomous materials science in revolutionizing our understanding of materials synthesis and degradation in extreme environments. By integrating advanced microscopy techniques, artificial intelligence, and robotic experimentation, we can accelerate the discovery and design of resilient materials for a sustainable future. The presentation will highlight recent breakthroughs in autonomous microscopy, computer vision, and machine learning, showcasing their ability to unravel complex material transformations at the atomic scale. The talk will also delve into the challenges and opportunities associated with deploying autonomous systems to probe extreme environments, emphasizing the importance of robust algorithms, real-time data analysis, and adaptive experimentation. Our ultimate goal is to empower scientists with unprecedented capabilities to explore, understand, and engineer materials that can withstand the harshest conditions, paving the way for innovations in energy, aerospace, and beyond.
Original languageAmerican English
Number of pages47
StatePublished - 2024

Publication series

NamePresented at the Colorado School of Mines Materials and Metallurgical Engineering Department Seminar, 14 November 2024, Golden, Colorado

NREL Publication Number

  • NREL/PR-5K00-92008

Keywords

  • artificial intelligence
  • electron microscopy
  • machine learning
  • materials science
  • order-disorder

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