five

Foundry - Approaching QMC quality energetics throughout chemical space using scalable quantum machine learning

收藏
DataCite Commons2024-02-26 更新2025-04-15 收录
下载链接:
https://www.materialsdatafacility.org/detail/foundry_qmc_ml_v1.1
下载链接
链接失效反馈
官方服务:
资源简介:
This dataset contains summary inputs and outputs generated for the Paper "Approaching QMC quality energetics throughout chemical space using scalable quantum machine learning" By B. Huang, O. Anatole von Lilienfeld, J. T. Krogel and A. Benali. Included in the dataset are energies for 1175 molecules calculated with varying methods, associated error calculations, and molecular structures in XYZ and pymatgen Molecule formats. Raw data for these calculations are available at https://doi.org/10.18126/hxlp-v732
提供机构:
Materials Data Facility
创建时间:
2022-10-12
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作