19.01.2021 von 16:15 bis 17:45

Zoom-Videokonferenz

In machine learning, energy-based models are rooted in concepts common to magnetism, like the Ising model. Within these models, plasticity, learning, and ultimately pattern recognition can be linked to the dynamics of coupled spin ensembles that exhibit complex energy landscapes akin to behavior seen in spin glasses. While this behavior is commercially emulated in software, there are strong pursuits to implement these concepts directly and autonomously in solid-state materials. To date, hybrid approaches, which often use the serendipitous electric, magnetic, or optical response of materials, emulate machine learning functionality with the help of external computers. Yet, there is still no clear understanding of how to create machine learning functionality from fundamental physical concepts in materials, like hysteresis, glassiness, or spin dynamics. This motivates new fundamental investigations of complex spin systems, and how their behavior can be manipulated to potentially new paradigms.

Based on scanning tunneling microscopy, magnetic atoms and films on surfaces have become a model playground to understand and design magnetic order. However, these model systems historically have been probed in limits for robust memory applications, namely strong double-well regimes. In this talk, I will illustrate new model platforms to realize machine learning functionality directly in the dynamics of coupled spin ensembles that exhibit multi-modal landscapes. I will first review the concept of energy- based neural networks and how they are linked to the physics of spin glasses. I will then highlight new examples based on the recent discovery of the so-called spin Q glass and the atomic Boltzmann machine. I will illustrate the creation of atomic-scale neurons and synapses, in addition to new learning concepts based on the separation of time scales and self-adaptive behavior. I will also discuss recent cutting-edge developments that enable magnetic characterization in new extreme limits and how this platform may be applied toward autonomous adaption and quantum machine learning.

Einladender: Prof. Heinze

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