End-to-end prediction and design of additively manufacturable alloys using a generative AlloyGPT model
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Abstract
Abstract Rapid progress in additive manufacturing of alloys opens opportunities in controlling compositions and microstructures at voxel-size resolution in complex geometries, thus unlocking unprecedented design and performance in various critical engineering applications. However, to fully exploit such potential, capable yet efficient models for navigating the vast design spaces of alloy compositions, structures and properties are of great research interest. Here, we present AlloyGPT, an autoregressive alloy-specific language model, that learns the composition-structure-property relationship and generates novel designs for additively manufacturable alloys. Specifically, we develop efficient grammar to convert physics-rich alloy datasets into readable text records for both forward prediction and inverse design tasks. Then, we construct a customized tokenizer and generative pre-trained transformer (GPT) model to master this alloy-specific language through autoregressive training. At deployment, our model can accurately predict multiple phase structures and properties based on given alloy compositions, achieving R2 values ranging from 0.86 to 0.99 for the test set. When tested beyond the learned composition domain, this performance only degrades gradually in a stable manner. Given the desired properties and structures, the same model can suggest multiple alloy compositions that meet the design goals. And the balance between composition diversity and design accuracy can be further tuned stably. Our AlloyGPT model presents a novel way of integrating comprehensive knowledge of alloys in terms of language and can simultaneously solve forward prediction and inverse design tasks with accuracy, diversity and robustness. This fundamental language model will open new avenues to accelerate knowledge integration and material design for pure or gradient structural alloys manufactured by traditional and additive manufacturing.
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- Authors
- Ni, B., Glaser, B., & Taheri-Mousavi, S. M.
- Affiliations
- Carnegie Mellon University (US)
- Corresponding author
- Taheri-Mousavi, S. Mohadeseh
- Venue
- npj Computational Materials
- Published
- 2025-09-26
- Volume
- 11
- Issue
- 1
- ISSN
- 2057-3960 (ISSN-L)
- OpenAlex
- W4414541773
- Cited by
- 23
- References
- 9
- Citations by year
- 2026: 19 · 2025: 4
- FWCI
- 4.86
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- npj Computational Materials (publishedVersion, cc-by-nc-nd)
- Research Square (acceptedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Naval Nuclear Laboratory (1047622)
- Topics
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Engineering › Mechanical Engineering › Thermodynamic and Structural Properties of Metals and Alloys
- Physical Sciences › Materials Science › Materials Chemistry › Material Selection and Properties
- Keywords
- generative language models, additive manufacturing of alloys, property prediction, composition-structure-property relationships, attention patterns, material design
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