Generative Chemical Language Models for Energetic Materials Discovery

arXiv cs.CL / 4/7/2026

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Key Points

The paper introduces generative molecular language models aimed at accelerating energetic materials discovery despite limited high-quality training data.
It uses transfer learning: pretraining on large-scale chemical data followed by fine-tuning on curated energetic materials datasets to move beyond prior focus on the pharmacological domain.
The authors propose fragment-based molecular encodings to improve the generation of synthetically accessible structures.
Overall, the work frames a general framework for other data-scarce discovery problems and targets next-generation energetic materials with stringent performance requirements.

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.