| Abstract Scope |
Despite the significant progress in experimental characterization techniques, understanding the microscopic interaction mechanisms in complex material families remains a grand challenge. Machine learning (ML) brings new hope and can even serve as a new probe to study the complex interplay between the charge, orbital, spin, and lattice degrees of freedom. In this talk, I will introduce how ML can be used to reveal the hidden information in experimental data and elucidate the microscopic interactions. I will provide a few examples from our research, that 1)how ML can help identify a nuanced effect that can lead to electronics without energy dissipation, 2)how ML can be used to rapidly screen materials with superior thermal properties, and lastly, 3)how ML can result in interfacial defects identification and hidden phonon transport with unprecedented knowledge. We highlight the importance of the representations and envision a variety of measurement problems that can benefit from machine learning. |