Fast phonon-aware scoring and reranking for crystal candidate pools
PhononScore is a web-based service for fast phonon-aware scoring and dynamical-stability reranking of crystalline materials, with a particular focus on generated crystal candidate pools. PhononScore is developed by Xiao-Qi Han, Ze-Feng Gao, and Zhong-Yi Lu from the School of Physics, Renmin University of China (Lu Group Homepage).
Upload a ZIP archive containing CIF, VASP, POSCAR, or CONTCAR files. The service converts valid structures to CIF, runs PhononScore or PhononScore-DFT, and returns a ranked CSV table.
PhononScore is currently offered as a free service for academic research. If this tool contributes to your work, we would greatly appreciate a citation of our related publications. The information collected below is used only for aggregated usage statistics and will not be made public. Your support through citations and acknowledgements directly encourages our continued development of fast and reliable computational tools.
Example: Renmin University of China - Xiao-Qi Han - hanxiaoqi@ruc.edu.cn
Auto mode uses uploaded-pool statistics for at least 100 structures and fixed benchmark statistics for smaller submissions.
Download the ZIP archive after scoring is finished. It contains the ranked CSV, logs, and converted CIFs.
.cif, .vasp, POSCAR, and CONTCAR.failed_structures.csv.PhononScore is developed within the PhononBench and InvDesFlow ecosystem for fast dynamical-stability-aware screening in AI-driven crystal generation. If you use PhononScore, PhononBench, or related components in your research, please consider citing the following works.
@misc{han2026phononscore,
title={PhononScore: a phonon-aware scoring function for dynamical stability},
author={Xiao-Qi Han and Ze-Feng Gao and Zhong-Yi Lu},
year={2026},
eprint={2607.08518},
archivePrefix={arXiv},
primaryClass={cond-mat.mtrl-sci},
url={https://arxiv.org/abs/2607.08518}}
@misc{han2025phononbench,
title={PhononBench: A Large-Scale Phonon-Based Benchmark for Dynamical Stability in Crystal Generation},
author={Xiao-Qi Han and Peng-Jie Guo and Ze-Feng Gao and Zhong-Yi Lu},
year={2025},
eprint={2512.21227},
archivePrefix={arXiv},
primaryClass={cond-mat.mtrl-sci},
url={https://arxiv.org/abs/2512.21227}}
@article{InvDesFlow-AL,
author={Xiao-Qi Han and Peng-Jie Guo and Ze-Feng Gao and Hao Sun and Zhong-Yi Lu},
title={InvDesFlow-AL: active learning-based workflow for inverse design of functional materials},
journal={npj Computational Materials},
year={2025},
volume={11},
number={1},
pages={364},
doi={10.1038/s41524-025-01830-z},
url={https://doi.org/10.1038/s41524-025-01830-z}}
@article{InvDesFlow,
title={InvDesFlow: An AI-Driven Materials Inverse Design Workflow to Explore Possible High-Temperature Superconductors},
journal={Chin. Phys. Lett.},
volume={42},
number={4},
pages={047301},
year={2025},
doi={10.1088/0256-307X/42/4/047301},
url={http://cpl.iphy.ac.cn/en/article/doi/10.1088/0256-307X/42/4/047301},
author={Xiao-Qi Han and Zhenfeng Ouyang and Peng-Jie Guo and Hao Sun and Ze-Feng Gao and Zhong-Yi Lu}}
@article{AI4Mreview,
title={AI-Driven Inverse Design of Materials: Past, Present, and Future},
journal={Chin. Phys. Lett.},
volume={42},
number={2},
pages={027403},
year={2025},
doi={10.1088/0256-307X/42/2/027403},
url={http://cpl.iphy.ac.cn/en/article/doi/10.1088/0256-307X/42/2/027403},
author={Xiao-Qi Han and Xin-De Wang and Meng-Yuan Xu and Zhen Feng and Bo-Wen Yao and Peng-Jie Guo and Ze-Feng Gao and Zhong-Yi Lu}}