Machine Learning-Assisted High-Throughput Virtual Screening for On-Demand Customization of Advanced Energetic Materials

Abstract

Finding energetic materials with tailored properties is always a significant challenge due to low research efficiency in trial and error. Herein, a methodology combining domain knowledge, a machine learning algorithm, and experiments is presented for accelerating the discovery of novel energetic materials. A high-throughput virtual screening (HTVS) system integrating on-demand molecular generation and machine learning models covering the prediction of molecular properties and crystal packing mode scoring is established. With the proposed HTVS system, candidate molecules with promising properties and a desirable crystal packing mode are rapidly targeted from the generated molecular space containing 25 112 molecules. Furthermore, a study of the crystal structure and properties shows that the good comprehensive performances of the target molecule are in agreement with the predicted results, thus verifying the effectiveness of the proposed methodology. This work demonstrates a new research paradigm for discovering novel energetic materials and can be extended to other organic materials without manifest obstacles.