A generalizable, similarity-driven sampling approach, powered by a novel stage-wise optimization strategy, to recover atom types, atomic positions, and unit cell shapes directly from a descriptor without any prior structural knowledge is proposed.
Abstract
Data-driven approaches to materials discovery rely on numerical representations of atomic structures as input for machine learning models. Inverting these descriptors - recovering atomic structures from their representations - is essential for most generative material design pipelines, yet it remains challenging, particularly for periodic systems. Existing inversion methods are either tailored to specific invertible descriptors or require candidate structures with similar atomic arrangements and compositions, limiting the exploration of novel regions in chemical and configurational space. Here, we propose a generalizable, similarity-driven sampling approach, powered by a novel stage-wise optimization strategy, to recover atom types, atomic positions, and unit cell shapes directly from a descriptor. Our approach requires only descriptor features and parameters as input without any prior structural knowledge. The capability of our method is demonstrated by the averaged Smooth Overlap of Atomic Positions (SOAP) descriptor.
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