PRISMat: Policy-Driven, Permutation-Invariant Autoregressive Material Generation

Computer Science > Artificial Intelligence [Submitted on 15 May 2026] PRISMat: Policy-Driven, Permutation-Invariant Autoregressive Material Generation Claire Schlesinger, Circe Hsu, Peter Schindler, Robin Walters Rapid identification of candidate materials with target properties has become a key task in materials science. Machine learning has emerged as an alternative to physics-based simulation, offering a faster and cheaper way to filter materials based on their stability and other target properties, reducing the number of candidates that reach the costly synthesis stage. Recently, Large Language Models (LLMs) have been applied to this role, but these models are parameter-heavy and computationally expensive both during training and at inference time, making them unsuitable for high-throughput tasks. This inefficiency stems from both the large over-parameterization of language models and the difficulty of framing material generation as a sequence learning problem. In this paper, we present PRISMat, a cost-effective, permutation-invariant model, which addresses these limitations. We show that PRISMat, despite taking less time for inference, is able to outperform LLMs in generating crystal slabs conditioned on critical materials' surface properties. In targeted material discovery, we achieve mean absolute errors of 0.188 eV/A^2 and 2.79 eV for cleavage energy and work function tasks, respectively, reducing the error of the next best model by 4\times. Comments: 10 pages, 8 figures, Under Review at Neurips 2026 Subjects: Artificial Intelligence (cs.AI); Materials Science (cond-mat.mtrl-sci) Cite as: arXiv:2605.16612 [cs.AI]   (or arXiv:2605.16612v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2605.16612 Focus to learn more Submission history From: Claire Schlesinger [view email] [v1] Fri, 15 May 2026 20:27:11 UTC (590 KB) Access Paper: HTML (experimental) view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-05 Change to browse by: cond-mat cond-mat.mtrl-sci cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)