Chemical Kinetics Database Translation for Machine-Learning-based Algorithm Development

Author(s):

Lydia G Yeh, Jeffrey D Veals, and Christopher P Stone

Abstract:

The efficacy of various chemical descriptor languages has been considered as part of a larger effort to develop a protocol to modernize and standardize entries in a large “in-house” database of chemical species information. This capability will help to improve the turnaround time of the data flow for US Army Combat Capabilities Development Command Army Research Laboratory chemical kinetics mechanism development efforts and make it possible to merge the in-house databases with additional open-source computational models. Several approaches have been considered to convert key information contained within Gaussian quantum chemistry simulation output files into several standardized formats including Simplified Molecular-Input Line-Entry System (SMILES), Canonical SMILES, InChlKeys, and Sybyl MOL2. Additional consideration has been made of the optimal contents and formatting of the full molecular species database moving forward. A brief overview of the initial protocol and progress to date is reported.