Physics-Constrained Learning of Dose-Dependent Spectral Degradation in Metal--Organic Frameworks from In Situ Low-Loss EELS
Gabriel T. dos Santos, Roberto dos Reis, Vinayak P. Dravid

TL;DR
This study employs a physics-informed neural network to model dose-dependent spectral degradation in metal-organic frameworks using in situ low-loss EELS, revealing key insights into material stability and spectral changes.
Contribution
It introduces a novel PINN-based framework that quantitatively links in situ spectroscopy to beam-induced degradation in MOFs, incorporating spectral descriptors and degradation equations.
Findings
C--O and C--C linkers are most dose-sensitive.
Half-integrity thresholds are around 1000 e$^-$/ ext{ A}$^2$.
Low-energy $ ext{ extpi}$--$ ext{ extpi}^*$ response involves oscillator-strength redistribution.
Abstract
Electron-beam irradiation limits atomic-resolution characterization of beam-sensitive hybrid materials, yet quantitative models that connect \textit{in situ} spectroscopy to dose-dependent degradation remain scarce. Here we use a physics-informed neural network (PINN) to model beam-induced spectral evolution in MIL-101(Fe) from an in situ low-loss electron energy-loss spectroscopy (EELS) dose series. Each spectrum is reduced to fixed-window low-loss descriptors, , evaluated over nominal --, C--C, C--O, and M--O windows. These descriptors are relative window-integrated low-loss spectral areas, not absolute f-sum-rule effective electron numbers. For each spectral channel, a latent integrity variable obeys the same uncoupled power-law degradation equation in normalized dose space, $dC_i/d\phi=-k_i…
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