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AI for Materials Discovery in Practice: Effect of low data on RL approaches

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SCLW02 - Reinforcement Learning for Science: Discovery and Automation

Novel materials have enabled industries and technologies: LEDs, electric motors and batteries. Their development is glacially slow, often taking decades. How do we speed up materials discovery using ML and AI tools. This talk will break down the extreme data challenges of materials discovery. How do we best gain information from 10 or less data points? How do we do that efficiently while accepting that science operates in an exceptionally complex and high-dimensional space? How do we take the correct sequence of decisions that maximise the synthesis outcome for a novel material? How do we bring as much of this complexity ‘in-distribution’? The talk will discuss both traditional ML, LLM -based, and mixed approaches with a holistic view on materials discovery.

This talk is part of the Isaac Newton Institute Seminar Series series.

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Plommer House A, Wolfson College Conference Room, 3rd Floor, Central Library, Cambridge CB2 3QD Seminar Room FG5, level 5 in the F & G wards, Addenbrooke's Hospital

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