Generative Chemical Language Models for Energetic Materials Discovery
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866914444798328832 |
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| author | Salij, Andrew Ullberg, R. Seaton Davis, Megan C. Cawkwell, Marc J. Snyder, Christopher J. Cardona, Cristina Garcia Matanovic, Ivana Kort-Kamp, Wilton J. M. |
| author_facet | Salij, Andrew Ullberg, R. Seaton Davis, Megan C. Cawkwell, Marc J. Snyder, Christopher J. Cardona, Cristina Garcia Matanovic, Ivana Kort-Kamp, Wilton J. M. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_03304 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Generative Chemical Language Models for Energetic Materials Discovery Salij, Andrew Ullberg, R. Seaton Davis, Megan C. Cawkwell, Marc J. Snyder, Christopher J. Cardona, Cristina Garcia Matanovic, Ivana Kort-Kamp, Wilton J. M. Chemical Physics Materials Science Artificial Intelligence Computation and Language Machine Learning 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. |
| title | Generative Chemical Language Models for Energetic Materials Discovery |
| topic | Chemical Physics Materials Science Artificial Intelligence Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2604.03304 |