Rapid Generative Discovery of High‐Energy Molecules in Low Data Regimes Using Minimal Computational Resources
TL;DR
This work presents a novel approach toward high‐energy molecules by combining long short‐term memory (LSTM) networks for molecular generation and attentive graph neural networks (GNN) for property predictions by combining fixed SHA‐256 embeddings with partially trainable representations.
Abstract
High‐energy materials (HEMs) are critical for propulsion and defense domains, yet their discovery remains constrained by experimental data and restricted access to testing facilities. This work presents a novel approach toward high‐energy molecules by combining long short‐term memory (LSTM) networks for molecular generation and attentive graph neural networks (GNN) for property predictions. We propose a transformative embedding space construction strategy that integrates fixed SHA‐256 embeddings with partially trainable representations. Unlike conventional regularization techniques, this changes the representational basis itself, reshaping the molecular input space before learning begins. Without recourse to pretraining, one of the proposed models for low data regime achieves 74.3% validity and 47.1% novelty. The generated library exhibits a mean Tanimoto coefficient of 0.214 relative to the training set signifying the ability of framework to generate a diverse chemical space. We identified 37 promising candidates for further research, all exhibiting a predicted detonation velocity greater than 9 .