Automated Peak Annotation in Time-of-Flight Secondary Ion Mass Spectrometry via a Physics-Informed Probabilistic Framework

Peak annotation in Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS) is a persistent bottleneck that typically requires the manual assignment of chemical formulas to hundreds of fragment ion peaks per spectrum. This work describes a physics-informed probabilistic framework that automates this task by combining five chemically motivated constraints—Gaussian mass accuracy, element composition priors, isotope pattern matching, nitrogen rule parity, and graded valence bounds—into a multiplicative belief score. We evaluate the framework on 643 ground-truth peaks from 151 compounds spanning both positive and negative ion modes, and we explicitly distinguish two regimes. As a scoring task—when the correct formula is present in the candidate list—the framework attains 52.3% Top-1 and 76.4% Top-3 accuracy, a 4.9-fold improvement over mass-only scoring. In fully automated end-to-end deployment, where candidates are generated de novo, Top-1 accuracy is 26.3%; the limiting factor is candidate generation rather than scoring, as only 46.5% of ground-truth formulas are currently produced by the database and combinatorial generator. Leave-One-Compound-Out Cross-Validation (59 compounds, 525 peaks) yields 51.8% Top-1 accuracy with fixed domain-knowledge weights, confirming generalization stability. Ablation analysis identifies element composition priors as the dominant non-mass constraint (−27.7 percentage points when removed), followed by isotope matching (−10.3 pp) and the nitrogen rule (−5.3 pp). The framework requires no labeled training spectra—relying instead on physically motivated priors and curated fragment databases—provides interpretable per-constraint scores (which represent relative rankings rather than calibrated probabilities), and supports polarity-specific configurations, offering a practical computational foundation for automated ToF-SIMS spectrum interpretation.