Automating New Materials Discovery: How UL Research Institutes & Uncountable use AI, Machine Learning, and High-Throughput Experimentation to Accelerate the Pace of Discovery
This joint session will demonstrate how UL’s Materials Discovery Research Institute and Uncountable’s AI-driven informatics platform collaborate to create an advanced materials discovery pipeline. Attendees will learn how high-throughput experimentation, advanced simulation, and AI-guided decision-making can rapidly explore vast chemical spaces, prioritize the most promising experiments, and feed results back into subsequent iterations. By integrating automated synthesis and characterization with data-centric machine-learning tools, UL Research Institutes and Uncountable aim to reduce discovery timelines from decades to years, enabling the rapid development of materials that address climate- and safety-related challenges.
Key Learning Objectives:
Integrating AI, machine learning, and high-throughput experimentation into a seamless pipeline for rapid materials innovation
Leveraging automated synthesis, characterization, and simulation to efficiently explore vast chemical spaces
Prioritizing high-impact experiments using data-driven decision-making to dramatically reduce discovery timelines
Enabling the rapid development of advanced materials that address critical climate and safety challenges
Who Should Attend:
Laboratory managers
Chromatographers
New product developers
Brought to you by:
Speakers
Akshay Talekar Data Science Lead, UL MDRI
Kelly McSweeney Contributing Editor, C&EN Media Group
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