(364b) Message Passing Neural Networks for Prediction of IR Spectra
AIChE Annual Meeting
Tuesday, November 9, 2021 - 3:42pm to 3:54pm
The presented model for IR spectra prediction requires only the input of a molecular SMILES strings to generate predictions. The chemical representation is processed through a message passing neural network to encode the molecule in a latent vector representation followed by a feed forward neural network for prediction of IR spectra. The entire process is differentiable, making even the encoding of the latent molecule vector learnable and optimizable. The model is pre-trained using semi-empirical quantum chemistry calculations (GFN2-xTB) for molecules sampled from the PubChem database to learn molecular encodings for a wide scope of molecules. Further training is performed using 56,955 experimental spectra collected from four data sources: the National Institute of Standards and Technology (NIST), Pacific Northwest National Labs (PNNL), The National Institute of Advanced Industrial Science and Technology (AIST), and the Coblentz Society. The model allows for predictions of spectra in the gas phase and in four supported condensed phases.