Abstract: In this informal talk, I will discuss our efforts in modeling, understanding and controlling nanomaterials and interfaces out-of-equilibrium using first principles theory over the years. Specifically, I will show how quantum effects in materials under light and biases can lead to strongly non-thermal and time-non-local responses. I will also describe our efforts in deploying machine learned surrogates of those responses as well as reinforcement learning approaches compatible with bias and illumination protocol optimization enabling the stabilization of non-equilibrium states. Finally, I will show how such responses can be leveraged in physical analog implementations of neural networks.
Series: The Argonne Microelectronics Institute brown bag seminar series is an informal discussion that enables information sharing and collaboration around microelectronics efforts. Join us in person or online to learn more about microelectronics research across Argonne.
If you have questions about the brown bag meeting series or have suggestions for future speakers, contact Swann Perarnau (swann@anl.gov).