High-throughput design of energetic molecules

Abstract

High-throughput design offers a promising way to expedite the de novo design of novel energetic molecules, but achieving this goal necessitates accurate methods for property prediction and efficient schemes for molecular screening. Two approaches for generating energetic molecules are proposed, based on a generative model and a fragment docking scheme, respectively. A high-throughput computation (HTC) workflow based on quantum chemistry is developed for energetic molecule design. Machine learning models are established for predicting crystal density, enthalpy of formation, R–NO₂ bond dissociation energy, detonation velocity, detonation pressure, detonation heat, detonation volume and detonation temperature, yielding coefficients of determination (R²) of 0.928, 0.948, 0.984, 0.989, 0.986, 0.986, 0.990 and 0.995, respectively. Thereby, an easy-to-use platform named Energetic Materials Studio (EM-Studio) integrates all the methods and models. Therein, five modules, EM-Generator, EM-QC, EM-DB, EM-ML and EM-Visualizer, work for molecule generation, HTC-aided molecule design, data management, machine learning prediction, and human–computer interaction, respectively. The effectiveness and capabilities of EM-Studio in HTC- and AI-aided molecular design are demonstrated through two cases of fused-ring energetic molecules.