A collaborative research initiative comprising Friedrich Schiller University Jena, the Helmholtz-Zentrum Berlin for Materials and Energy, the Helmholtz Institute for Polymers in Energy Applications Jena, and Swiss software firm Zakodium Sárl has introduced SECS, an artificial intelligence platform engineered to accelerate molecular structure elucidation. Recently published in Nature Communications, the system translates raw spectroscopic measurements into proposed chemical architectures within minutes, addressing a persistent bottleneck in analytical chemistry. The development directly confronts the traditional difficulty of verifying molecular composition using nuclear magnetic resonance, infrared spectroscopy, and mass spectrometry. Interpreting these analytical signals frequently resembles solving a complex puzzle, particularly when evaluating novel compounds or managing experimental impurities that obscure target data. SECS resolves these limitations by deploying a dual computational strategy. A machine learning framework converts spectral data and molecular structures into a shared mathematical space, while an evolutionary algorithm iteratively optimizes candidate molecules by modifying atomic arrangements until the simulated output aligns with the experimental measurements. Independent validation confirms the platform's analytical precision. Cross-method benchmarks yielded correct top-ranked predictions in more than eighty percent of cases. Furthermore, a pilot assessment pitting the system against professional chemists tasked with twenty difficult NMR interpretation problems demonstrated performance parity with human specialists. The research team explicitly positions SECS as a computational second opinion rather than an autonomous replacement. High-confidence predictions validate researcher hypotheses, whereas substantial discrepancies trigger targeted reevaluation of sample purity or experimental parameters. The consortium has made the source code, model parameters, and a functional web application publicly accessible to the scientific community. The current architecture specializes in processing one-dimensional proton NMR datasets, with development roadmaps indicating future integration of multi-dimensional spectral analysis and support for more complex raw data streams. By automating initial structure verification and augmenting established laboratory workflows, SECS establishes a scalable efficiency benchmark for chemical research, materials science, and pharmaceutical development pipelines.