IR-NMR multimodal computational spectra dataset for 177K patent-extracted organic molecules.

ABSTRACT: The construction of predictive models in molecular science increasingly relies on large, high-quality datasets. Synthetic data generation is becoming a foundational strategy for advancing model accuracy and enabling fast discovery workflows. To support the development of structure elucidation and spectral property prediction models, we present a comprehensive synthetic dataset of infrared (IR) and nuclear magnetic resonance (NMR) spectra for a diverse ensemble of organic molecules. The data were generated using a hybrid computational approach that integrates molecular dynamics (MD) simulations, density functional theory (DFT) calculations, and machine learning (ML) models. The dataset primarily consists of IR spectra for 177,461 molecules, derived from long-timescale MD simulations with ML

SUBMITTER: Zipoli F 

PROVIDER: S-EPMC12331906 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature