AC Seminar: Artem Mishchenko
AI is transforming materials discovery, but turning digital predictions into physical reality remains a major challenge. Join Artem Mishchenko of Google DeepMind for a look at how experimental-first AI, automated laboratories, and high-fidelity data—including negative results—could reshape the future of materials discovery.
Abstract: While AI has revolutionized our ability to predict materials in silico, the transition from digital discovery to physical reality remains the primary bottleneck. Most existing AI models are trained on computational datasets that often diverge from experimental truth. To unlock the next generation of materials, we must pivot toward the creation of high-fidelity, experimental datasets. This talk discusses the necessity of "experimental-first" AI strategies, focusing on how automated laboratories and standardized data capture - including the often-overlooked "negative results" - are essential for building models that can truly navigate the complex landscape of physical matter.
Bio: Artem Mishchenko is a Senior Staff Laboratory Scientist at Google DeepMind and an Honorary Professor at the University of Manchester. He leads the creation of the first experimental laboratory at Google DeepMind dedicated to closing the loop in materials discovery by integrating AI with automated synthesis and characterisation. His background is in 2D materials, with a PhD in molecular electronics from the University of Bern.
About the AC Seminar Series
The Acceleration Consortium (AC) seminar series explores perspectives on the future of AI for science, presents cutting-edge research findings, enables collaborations, and offers training and upskilling opportunities. Presented both in-person and online, these seminars will host a diverse set of speakers on topics related to accelerated discovery across three tracks:
AC Distinguished Seminars: Leaders in the autonomous discovery community will share their findings and perspectives that are helping to shape future directions and address key challenges. These will be delivered in a hybrid format at the University of Toronto.
AC Early Career Seminars: Early career researchers will present results from their latest publications, taking a technical dive into findings, methods, and tools. These will be delivered in a hybrid format at the University of Toronto.
AC Virtual Training Seminars: Instructors will provide standalone introduction lectures and hands-on tutorials on topics related to self-driving labs with an emphasis on principles, literacy, and skills. These will be delivered virtually.
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