Generative Chemical Language Models for Energetic Materials Discovery

• URL: http://arxiv.org/abs/2604.03304v1
• Date: Mon, 30 Mar 2026 18:04:58 GMT
• Title: Generative Chemical Language Models for Energetic Materials Discovery
• Abstract summary: Discovery of new energetic materials remains a pressing challenge hindered by limited availability of high-quality data.<n>We have developed generative molecular language models that have been pretrained on extensive chemical data and then fine-tuned with curated energetic materials datasets.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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