Local molecular orientation and conformation within nanostructures is central to their behavior in devices, emergent quantum states, and biological processes. X-ray microscopy and scattering at the carbon absorption edge is uniquely sensitive to these phenomena, through transition dipole moments encoded with photon energy and polarization. However, quantitative analysis is precluded due to the lack of accurate optical models with bond and orientation specificity. We generate such a model through an unsupervised machine learning algorithm that clusters density functional theory calculations into principle electronic transitions, quantitatively refined with angle-resolved absorbance measurements. The resulting optical tensor reproduces data from samples with domains of different orientation and crystalline packing. This refinement method connects spectral features to chemical moieties and resolves measurement-inaccessible dipole moments, establishing a new paradigm for label-free orientation analyses of bonds within molecular nanostructures.

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• Crystal structure
• Dielectric properties
• Electronic excitation & ionization
• Electronic structure
• Electronically polarized systems
• Energy materials
• Functional materials
• Light-matter interaction
• Molecular spectra
• Organic electronics
• Scattering of atoms, molecules, clusters & ions
• Single- and few-photon ionization & excitation