PhononBench:A Large-Scale Phonon-Based Benchmark for Dynamical Stability in Crystal Generation
Xiao-Qi Han, Peng-Jie Guo, Ze-Feng Gao, Zhong-Yi Lu

TL;DR
PhononBench introduces a large-scale benchmark for evaluating the dynamical stability of AI-generated crystal structures using phonon predictions, revealing current models' limitations and providing a valuable resource for future materials discovery.
Contribution
This work presents the first large-scale phonon-based benchmark for crystal stability, utilizing MatterSim for DFT-level accuracy and analyzing over 108,000 generated structures.
Findings
Current generative models have an average stability rate of 25.83%.
MatterGen achieves a stability rate of 41.0%.
Higher symmetry crystals tend to be more stable, with cubic systems reaching 49.2% stability.
Abstract
In this work, we introduce PhononBench, the first large-scale benchmark for dynamical stability in AI-generated crystals. Leveraging the recently developed MatterSim interatomic potential, which achieves DFT-level accuracy in phonon predictions across more than 10,000 materials, PhononBench enables efficient large-scale phonon calculations and dynamical-stability analysis for 108,843 crystal structures generated by six leading crystal generation models. PhononBench reveals a widespread limitation of current generative models in ensuring dynamical stability: the average dynamical-stability rate across all generated structures is only 25.83%, with the top-performing model, MatterGen, reaching just 41.0%. Further case studies show that in property-targeted generation-illustrated here by band-gap conditioning with MatterGen--the dynamical-stability rate remains as low as 23.5% even at the…
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Taxonomy
TopicsMachine Learning in Materials Science · Thermal properties of materials · Topological Materials and Phenomena
