RESEARCH

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

ArXiv cs.AI · Tue, 19 May 2026 04:00:00 GMT

arXiv:2605.16612v1 Announce Type: new Abstract: 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 m

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