Responsive Disorder in a Metal-Organic Framework Enables Solid-State Reservoir Computing
Guy Greenbaum, Will R. Branford, Andrew L. Goodwin

TL;DR
This paper demonstrates that a disordered metal-organic framework can serve as a solid-state reservoir computer, leveraging its responsive disorder and configurational transitions for energy-efficient machine learning tasks.
Contribution
It introduces the concept of responsive disorder in solid materials as a basis for atomic-scale reservoir computing, expanding the scope of physical reservoir computing to disordered solid phases.
Findings
Disordered metal-organic framework DUT-8 exhibits disorder-disorder transitions upon guest exposure.
X-ray diffuse scattering variations can be used as readouts for machine learning.
The system achieves classification and time-series tasks with accuracy comparable to mesoscale reservoirs.
Abstract
Complex systems with nonlinear response mechanisms can be applied as reservoir computers for energy-efficient machine learning tasks. Historically explored at the macro- and meso-scale, physical reservoir computing has recently been extended to the atomic scale via chemical mixtures with strong and dynamic heterogeneity. Here we explore the possibility that configurational degeneracy within disordered materials might form the basis for solid-state atomic-scale reservoirs. Our proof-of-concept uses the disordered metal-organic framework DUT-8, which undergoes a series of disorder-disorder transitions on exposure to different guest species. We show that variations in X-ray diffuse scattering associated with these transitions function as suitable readouts for machine learning applications. A combination of nonlinearity and memory effects in the DUT-8 response allows the system to carry out…
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Taxonomy
TopicsNeural Networks and Reservoir Computing · Ferroelectric and Negative Capacitance Devices · Metal-Organic Frameworks: Synthesis and Applications
