[2604.03304] Generative Chemical Language Models for Energetic Materials Discovery

Physics > Chemical Physics arXiv:2604.03304 (physics) [Submitted on 30 Mar 2026] Title:Generative Chemical Language Models for Energetic Materials Discovery Authors:Andrew Salij, R. Seaton Ullberg, Megan C. Davis, Marc J. Cawkwell, Christopher J. Snyder, Cristina Garcia Cardona, Ivana Matanovic, Wilton J. M. Kort-Kamp View a PDF of the paper titled Generative Chemical Language Models for Energetic Materials Discovery, by Andrew Salij and 7 other authors View PDF HTML (experimental) 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. Subjects: Chemical Physics (physics.chem-ph); Materials Science (cond-mat.mtrl-sci); Artificial Intelligence (cs.AI); Computati...