This publication introduces a novel open-access 31P Nuclear Magnetic Resonance (NMR) shift database. With 14,250 entries encompassing 13,730 distinct molecules from 3,648 references, this database offers a comprehensive repository of organic and inorganic compounds. Emphasizing single-phosphorus atom compounds, the database facilitates data mining and machine learning endeavors, particularly in signal prediction and Computer-Assisted Structure Elucidation (CASE) systems. Additionally, the article compares different models for 31P NMR shift prediction, showcasing the database’s potential utility. Hierarchically Ordered Spherical Environment (HOSE) code-based models and Graph Neural Networks (GNNs) perform exceptionally well with a mean squared error of 11.9 and 11.4 ppm respectively, achieving accuracy comparable to quantum chemical calculations.

中文翻译：

---

Ilm-NMR-P31：开放访问的 31P 核磁共振数据库和数据驱动的 31P NMR 位移预测

本出版物介绍了一种新型开放式 31P 核磁共振 (NMR) 位移数据库。该数据库包含 14,250 个条目，涵盖来自 3,648 个参考文献的 13,730 个不同分子，提供了有机和无机化合物的综合存储库。该数据库强调单磷原子化合物，有助于数据挖掘和机器学习工作，特别是在信号预测和计算机辅助结构解析（CASE）系统中。此外，本文还比较了 31P NMR 位移预测的不同模型，展示了该数据库的潜在效用。基于分层有序球形环境 (HOSE) 代码的模型和图神经网络 (GNN) 表现非常出色，均方误差分别为 11.9 和 11.4 ppm，达到了与量子化学计算相当的精度。