AI/ML Enabled High‐Throughput Design and Synthesis for Energetic Molecules

ABSTRACT Energetic molecules are the key components in energetic materials, and the huge screening space of molecular structures make the design of energetic molecules complicated. In this work, we adopt a research method that combines High‐Throughput Computation (HTC), Chemical Informatics, classic Machine Learning (ML) and Artificial Intelligence (AI) generative models to achieve high‐throughput design of molecular structures for energetic materials. Based on the HTC results and molecular structural features extracted from chemical informatics, ML models for the structure–activity relationship of energetic molecules was obtained. Using the ML models, detonation parameters were predicted and target energetic molecules were screened in a short time. Meanwhile, using AI generative models, it was found that novel low‐sensitivity high‐energy molecules could be generated automatically; and their performance as well as synthetic accessibility were verified theoretically. Furthermore, the applications of ML models and AI in synthetic route design of energetic molecules are prospected. This research demonstrates the important role of AI/ML models in accelerating the development of energetic materials.