Generative Chemical Language Models for Energetic Materials Discovery

Abstract

The discovery of new energetic materials remains a pressing challenge hindered by limited availability of high-quality data. To address this, we have developed generative molecular language models that have been pretrained on extensive chemical data and then fine-tuned with curated energetic materials datasets. This transfer-learning strategy extends the chemical language model capabilities beyond the pharmacological space in which they have been predominantly developed, offering a framework applicable to other data-spare discovery problems. Furthermore, we discuss the benefits of fragment-based molecular encodings for chemical language models, in particular in constructing synthetically accessible structures. Together, these advances provide a foundation for accelerating the design of next-generation energetic materials with demanding performance requirements.

Figures (18)

• Figure 1: a) Training pipeline for GPT models, staged into pretraining to produce a wide variety of molecules and fine-tuning, which produces many C-, N-, O-containing compounds. b) Scheme of GPT model architecture and data processing.
• Figure 2: a) Synthetic accessibility (SA) score ertl2009estimation distributions for unconditioned molecular outputs of pretrained $\chi$hem- and fine-tuned X-GPT models with a) number of heavy atoms generated and b) predicted detonation velocities via ChemProp heid2023chemprop surrogate. All subfigures are normalized such that the highest histogram bin is 1.
• Figure 3: Comparison of estimated detonation velocities and pressures from Kamlet-Jacobs equations kamlet1968chemistry for unconditioned (left column) and conditioned generation (right) for fine-tuned GroupSELFIES models compared to the base model. All subfigures have been normalized to an identical maximum value.
• Figure 4: Distributions of a) number of nitrogen-oxygen bonds, b) number of nitrogen-nitrogen bonds, c) quantitative estimation of druglikeness (QED), and d) synthetic accessibility score (SA Score) in large SELFIES-based GPT models.
• Figure 5: Common substructures of generated output from chemical language models that have been manually selected for diversity. Substructures were chosen as representative samples from the 200 most common subgraphs between size $5-10$ as obtained via RDKit rdkitsoftware.