Data-Driven Property Estimation for Protective Clothing
Abstract
This report details an exploratory effort for making data-driven prediction of barrier properties, completed in 2013 at the U.S. Army Natick Research, Development and Engineering Center (NSRDEC), as part of the integrated protective fabric system (IPFS) project sponsored by the Defense Threat Reduction Agency (DTRA). Desorption data for 23 organic solvents ("data chemicals") from butyl rubber were measured and comprehensively analyzed to estimate diffusion coefficients. Using commercial computational chemistry software, machine-readable structures were built and numerous molecular descriptors calculated for these solvents and several threat agents and simulants ("query chemicals"). Matlab(R) codes were developed and probed to implement a machine learning technique --Artificial Neural Networks. Trained using the descriptors and diffusion coefficients of the data chemicals, the network is able to make good predictions for query chemicals for which only the descriptors are known. Cheminformatics --demonstrated in this work for characterizing threat agents --has a broader scope, in computational toxicology.
Document Details
- Document Type
- Technical Report
- Publication Date
- Sep 01, 2014
- Accession Number
- ADA614094
Entities
People
- Joseph Lavoie
- Ramanathan Nagarajan
- Sree Srinivasan
Organizations
- United States Army Soldier Systems Center