Generative structure search for efficient and diverse discovery of molecular and crystal structures

TL;DR

The paper introduces GSS, a unified generative framework combining diffusion models and random search to efficiently discover diverse molecular and crystal structures with lower computational cost.

Contribution

It presents a novel generative structure search method that integrates data priors and physical forces, enabling efficient exploration of energy landscapes beyond training data.

Findings

GSS recovers diverse metastable structures with over tenfold lower sampling cost.

It remains effective for compositions outside the training distribution.

GSS unifies diffusion-based generation and random search as a common sampling process.

Abstract

Predicting stable and metastable structures is central to molecular and materials discovery, but remains limited by the cost of searching high-dimensional energy landscapes. Deep generative models offer efficient structure sampling, yet their outputs remain shaped by training data and can underexplore minima that are rare but physically relevant. We introduce generative structure search (GSS), a unified framework that formulates diffusion-based generation and random structure search (RSS) as limiting regimes of a common sampling process driven by learned score fields and physical forces. Coupling these drivers lets GSS use data priors to accelerate sampling while retaining energy-guided exploration of local minima. Across molecular and crystalline systems, GSS recovers diverse metastable structures with more than tenfold lower sampling cost than RSS for broad coverage and remains effective for compositions outside the training distribution. The results establish a physically grounded generative search strategy for discovering structures beyond the reach of data-driven sampling alone.