Green Computational Approach for Raman Spectroscopy Prediction: A Sustainable Method for Hazardous Chemical Database Construction

Traditional experimental characterization of extremely hazardous chemical warfare agents poses serious safety and environmental challenges, including strict safety requirements, hazardous waste generation, and risks of researcher exposure. This study presents a green computational methodology using density functional theory (DFT) to construct Raman spectroscopy databases without physical synthesis or handling of these dangerous compounds. By comparing four computational method combinations against experimental data for eleven nerve agents, B3LYP/6‐311++G(d, p) was identified as the most accurate approach, showing high predictive performance with R 2 = 0.975–0.997 and root–mean‐squared error = 16.5–39.7 cm −1 . The validated method was applied to predict Raman spectra for five Novichok candidates, identifying characteristic P ═O, C═N, and P–F stretching bands that enable structural differentiation. In addition, a database of 81 new‐type derivatives was constructed through systematic alkyl substitution, demonstrating the scalability and efficiency of the approach. This green computational strategy provides a safer and more sustainable alternative to hazardous experimental procedures by minimizing waste, eliminating direct exposure risks, and supporting environmentally benign chemical defense research.