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PreprintCC BY 4.0arXiv

Physics-Constrained Learning of Dose-Dependent Spectral Degradation in Metal--Organic Frameworks from In Situ Low-Loss EELS

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, ñeff,j(Φ)=∫𝒲jS(E,Φ) dE, evaluated over nominal π--π*, 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 Ci(Φ) obeys the same uncoupled power-law degradation equation in normalized dose space, dCi/dϕ=−ki Cipi, regularized by monotonicity, boundedness, and a single hierarchy prior kC−O≥ kC−C. Applied to nine dose frames spanning 152--1368~e-/Å2, the ensemble PINN identifies C--O and C--C as the most strongly dose-sensitive linker-associated channels, with half-integrity thresholds of approximately 1.0×103~e-/Å2. The 1--3~eV π--π*-labelled window increases with dose and is therefore interpreted as a mixed low-energy response, likely involving oscillator-strength redistribution rather than direct monotonic loss of a single bond population. The framework provides a dose-dependent, spectroscopy constrained description of MOF degradation while also defining the limits of what fixed-window low-loss EELS can assign without independent chemical-state validation.

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BibTeX
@misc{santos2026physicsconstrained,
  title = {{Physics-Constrained Learning of Dose-Dependent Spectral Degradation in Metal--Organic Frameworks from In Situ Low-Loss EELS}},
  author = {Gabriel T. dos Santos and Roberto dos Reis and Vinayak P. Dravid},
  howpublished = {arXiv},
  year = {2026},
  month = sep,
  doi = {10.48550/arxiv.2605.01697},
  eprint = {2605.01697},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2605.01697},
  note = {CC BY 4.0},
}
RIS
TY  - UNPB
TI  - Physics-Constrained Learning of Dose-Dependent Spectral Degradation in Metal--Organic Frameworks from In Situ Low-Loss EELS
AU  - Gabriel T. dos Santos
AU  - Roberto dos Reis
AU  - Vinayak P. Dravid
PY  - 2026
DA  - 2026/09/30
PB  - arXiv
DO  - 10.48550/arxiv.2605.01697
UR  - https://arxiv.org/abs/2605.01697
AB  - 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, ñ_eff,j(Φ)=∫_𝒲_jS(E,Φ) dE, evaluated over nominal π--π^*, 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 C_i(Φ) obeys the same uncoupled power-law degradation equation in normalized dose space, dC_i/dϕ=−k_i C_i^p_i, regularized by monotonicity, boundedness, and a single hierarchy prior k_C−O≥ k_C−C. Applied to nine dose frames spanning 152--1368~e^-/Å^2, the ensemble PINN identifies C--O and C--C as the most strongly dose-sensitive linker-associated channels, with half-integrity thresholds of approximately 1.0×10^3~e^-/Å^2. The 1--3~eV π--π^*-labelled window increases with dose and is therefore interpreted as a mixed low-energy response, likely involving oscillator-strength redistribution rather than direct monotonic loss of a single bond population. The framework provides a dose-dependent, spectroscopy constrained description of MOF degradation while also defining the limits of what fixed-window low-loss EELS can assign without independent chemical-state validation.
ER  -