AtomBench: A Benchmark for Generative Atomic Structure Models using GPT, Diffusion, and Flow Architectures

Abstract

Generative models have become significant assets in the exploration and identification of new materials, enabling the rapid proposal of candidate crystal structures that satisfy target properties. Despite the increasing adoption of diverse architectures, a rigorous comparative evaluation of their performance on materials datasets is lacking. In this work, we present a systematic benchmark of three representative generative models- AtomGPT (a transformer-based model), Crystal Diffusion Variational Autoencoder (CDVAE), and FlowMM (a Riemannian flow matching model). These models were trained to reconstruct crystal structures from subsets of two publicly available superconductivity datasets- JARVIS Supercon 3D and DS A/B from the Alexandria database. Performance was assessed using the Kullback-Leibler (KL) divergence between predicted and reference distributions of lattice parameters, as well as the mean absolute error (MAE) of individual lattice constants. For the computed KLD and MAE scores, CDVAE performs most favorably, followed by AtomGPT, and then FlowMM. All benchmarking code and model configurations will be made publicly available at https://github.com/atomgptlab/atombench_inverse.