Classification software that derives theoretically calculated signatures/spectra of unknown, not yet created, toxic compounds.
The government seeks innovative methods to create theoretical spectroscopic signatures of potentially toxic chemical compounds for use in detection systems. Compounds of interest include chemical warfare agents (CWAs), toxic industrial compounds (TICs), pharmaceutical based agents (PBAs), and non-traditional agents (NTAs). Compounds of interest could be naturally occurring or synthetic. Novel classification, identification, and quantification methods can provide enormous savings in cost and timelines for fielding new detector systems and can improve the reliability and performance of both current and future systems. These enhancements will ultimately result in increased safety for the public and Department of Homeland Security operational units when encountering novel agents.
Detection systems that rely on target materials’ spectroscopic signatures have been limited to the detection, and possible quantification, of known compounds whose signatures have been measured experimentally. This project will introduce the ability to expand libraries of spectroscopic signatures beyond that limited set by (1) the automated generation of molecular structures, (2) theoretical prediction of their spectroscopic signatures, and (3) predictions of their toxicity metrics. This will dramatically expand the range of potentially toxic materials that may be detected, even with existing detection systems. Present technologies for spectrum prediction include the use of molecular dynamics to simulate single molecules and clusters of molecules, and density functional theory (DFT); some employ machine learning algorithms. However, these techniques still lack sufficient accuracy to fill the needs of the Department of Homeland Security.
The project entails developing theoretical spectra of toxic compounds, such as CWAs, TICs, PBAs, NTAs, and similar compounds. The work could proceed from low molecular weight to higher molecular weight compounds. Algorithms for classification may focus on a chosen spectroscopic technology and to provide tools to enable theoretically based identification. This effort is meant to develop algorithms; the choice of platform (e.g. cloud or edge computing) is up to the performer. Estimation of toxicity metrics of chemicals in the above-listed classes, including as-yet unknown threat agents, can be defined by immediately dangerous to life and health (IDLH) metrics following NIOSH/OSHA standards. Finally, data formats must be non-proprietary. Standard data formatting will enable efficient data processing and reachback analysis.