Understanding impact sensitivity of energetic molecules by supervised machine learning
Heather M. Quayle, Karthik Mohan, Sohan Seth, Colin R. Pulham, Carole A. Morrison
Abstract
Machine learning models have been developed to rationalise correlations between molecular structure and sensitivity to initiation by mechanical impact for a data set of 485 energetic molecules.
Topics & Concepts
Sensitivity (control systems)Artificial intelligenceMachine learningSet (abstract data type)Computer scienceTraining setSupervised learningData setPattern recognition (psychology)Experimental dataSupport vector machineComputational Drug Discovery MethodsEnergetic Materials and Combustionthermodynamics and calorimetric analyses