arXiv subjects

Guy Greenbaum

Publications and source records attributed to Guy Greenbaum.

2 recordsLinked to original sources

arXiv2026

Hidden Truchet Architecture in Zinc $p$-Hydroxybenzoate

We redetermine the structure of the disordered metal-organic framework Zn(hba) (hba$^{2-}$ = the dianion of 4-hydroxybenzoic acid). Using single-crystal X-ray diffraction measurements, we characterise the structured diffuse scattering that is experimentally observed for this material and which is characteristic of strongly correlated disorder. We use geometric and crystal chemical arguments to propose a general model for correlated disorder in Zn(hba), and then relate this model to a specific realisation of so-called Truchet tilings. Using Monte Carlo simulations, we proceed to show that the model so developed is simultaneously consistent with both the average crystal structure solution described previously, and the structured diffuse scattering reported here. The existence of ordered analogues with different, but related, chemistry suggests scope for control over correlated disorder in this family of metal-organic frameworks. Our study illustrates the potential for a Truchet-tile formalism to help describe and understand more generally the correlated disorder that occurs in framework materials - even amongst those that are chemically and crystallographically dissimilar.

cond-mat.mtrl-sci↗

arXiv2026

Responsive Disorder in a Metal-Organic Framework Enables Solid-State Reservoir Computing

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 both classification and time-series machine learning tasks with accuracies comparable to those of mesoscale physical reservoir computers. Our results suggest a new avenue for exploiting correlated disorder in solid phases whenever the nature of that disorder can be modulated through external perturbations-a phenomenon we term `responsive disorder'.

cond-mat.dis-nn↗