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Maya Martirossyan

2 accepted papers

2025

All that structure matches does not glitter

NeurIPS 2025poster

Generative models for materials, especially inorganic crystals, hold potential to transform the theoretical prediction of novel compounds and structures. Advancement in this field depends critically on robust benchmarks and minimal, information-rich datasets that enable meaningful model evaluation.…

Cited by 0SourceScholar
2025

Open Materials Generation with Stochastic Interpolants

ICML 2025poster

The discovery of new materials is essential for enabling technological advancements. Computational approaches for predicting novel materials must effectively learn the manifold of stable crystal structures within an infinite design space. We introduce Open Materials Generation (OMatG), a unifying fr…