Generative Chemical Language Models for Energetic Materials Discovery
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 ...
| Authors | Andrew Salij, R. Seaton Ullberg, Megan C. Davis et al. |
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| Primary category | physics.chem-ph |
| Published | 30 March 2026 |
| Last updated at source | 30 March 2026 |
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