Designing Quantum Materials using AI & Machine Learning

Artificial intelligence and machine learning (AI/ML) has sped up the discovery, synthesis, and characterization of quantum materials. The growth of this interdisciplinary field is driven by the collaboration of materials scientists, chemists, physicists, and more. This webinar will showcase advances from developing algorithms to understanding interactions in quantum materials to applying this knowledge in new technologies. Speakers will then discuss the interdisciplinary nature of AI/ML and collaborations with researchers, national labs, and companies. Maria K. Chan, Argonne Maria Chan obtained her PhD in Physics from MIT. Since 2012, Dr. Chan has been a staff scientist at Argonne’s Center for Nanoscale Materials. Her research focuses on the computational prediction of materials properties, using first principles, atomistic, and ML methods, particularly in applications toward energy technologies, such as energy storage, photovoltaics, catalysis, and thermal management. Stephen K. Gray, Argonne Stephen K. Gray is a Senior Scientist at Argonne’s Center for Nanoscale Materials. He has a PhD in Chemistry from UC Berkeley, and carried out post-doctoral work at Oxford and the University of Chicago. He is a Fellow of the American Physical Society, and his diverse research work is currently focused on modeling light-matter interactions in nanoscale materials, as well as hybrid materials for quantum information and quantum sensing. Alán Aspuru-Guzik, University of Toronto Alán Aspuru-Guzik is a Professor of Chemistry and Computer Science at the University of Toronto. He is a CIFAR AI Chair and CIFAR Fellow and Faculty member at the Vector Institute for Artificial Intelligence. He is interested in the intersections of quantum information and chemistry as well as the intersections of AI, automation and chemistry. He is a co-founder and officer of Zapata Computing, a quantum computing startup and Kebotix, a startup dedicated to self-driving laboratories.