AtomBench: A Benchmarking Framework for Generative Crystal Reconstruction Models in Conventional Superconductors
arXiv:2510.16165v2 Announce Type: replace Abstract: A key question in benchmarking generative crystal reconstruction models is how the amount and type of crystallographic information provided to a generative model affects its ability to reconstruct atomic structures. Yet such comparisons often overlook the fact that models receive unequal information about the target during reconstruction, thereby confounding architectural conclusions. We present AtomBench, an extensible, model-agnostic framework for comparing generative models on a well-defined crystal reconstruction task (rather than \textit
The proliferation of generative AI models necessitates robust and standardized benchmarking frameworks to ensure reliable comparisons and accelerate development in materials science.
This framework provides a critical tool for evaluating and improving generative AI models in crystal reconstruction, a foundational capability for advanced materials discovery and design (e.g., superconductors).
The ability to accurately compare generative models for crystal reconstruction will accelerate the discovery of new materials, improving design efficiency and reducing experimental trial-and-error.
- · Materials science researchers
- · AI model developers
- · Superconductor industry
- · Traditional materials discovery methods
Standardized benchmarking leads to more rapid iteration and improvement of generative crystal reconstruction models.
Accelerated material discovery, particularly for superconductors, impacts energy technology and computing at scale.
Enhanced material design capabilities could lead to breakthroughs in other fields dependent on novel materials, such as quantum computing components or advanced energy storage.
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Read at arXiv cs.LG