Molecular Energies Derived from Deep Learning: Application to the Prediction of Formation Enthalpies Up to High Energy Compounds

Didier Mathieu

2021Molecular Informatics19 citationsDOI

Abstract

using an atom equivalent (AE) scheme for a database of CHNO compounds. As expected from the accuracy of those models in predicting reference DFT frequencies and DLPNO-CCSD(T)/CBS energies, this procedure usually outperforms DFT-based AE schemes. However, for some compounds, including energetic molecules, significant deviations from experiment are observed, larger than obtained using DFT procedures. A close examination of the GDB-11 database from which the training data was drawn reveals that many structures of interest in the energetic materials community are excluded from this extensive compilation primarily focused on drug discovery and designed with stability constraints in mind. This points to the urgent need to set up a comparable database including energetic species of interest for the design of energetic materials such as propellants or explosives.

Topics & Concepts

Explosive materialPropellantStability (learning theory)Density functional theoryChemistryStandard enthalpy of formationDatabaseComputational chemistryComputer sciencePhysical chemistryMachine learningOrganic chemistryChemical Thermodynamics and Molecular StructureComputational Drug Discovery MethodsEnergetic Materials and Combustion