QM9star,两百万个DFT计算的离子和激素的平衡结构与原子信息
Miao-Jiong Tang1, Tian-Cheng Zhu1, Shuo-Qing Zhang2
1Center of Chemistry for Frontier Technologies, Department of Chemistry, Zhejiang University, Hangzhou, 310027, P. R. China.
这项研究介绍了QM9star,它是一个巨大的离子和基的量子化学数据集,对于理解化学反应至关重要. 这些数据支持用于化学中间体研究的机器学习.
科学领域:
- 计算化学是一种计算化学.
- 量子化学是一种量子化学.
- 化学信息学 化学信息学
背景情况:
- 化学中间体如离子和激素对于分子转换至关重要.
- 了解它们的特性是预测反应结果的关键.
- 现有的数据集可能无法全面覆盖这些反应性物种.
研究的目的:
- 为了介绍QM9star,一个新的量子化学数据集.
- 提供关于阳离子,阳离子和激素的广泛数据.
- 促进机器学习在化学中间体研究中的应用.
主要方法:
- 通过去除终端气,从QM9数据集结构中提取.
- 使用B3LYP-D3(BJ)/6-311+G(d,p) 密度函数理论进行了优化.
- 包括全球性质 (例如轨道能量) 和局部原子性质 (例如电荷,自旋密度).
主要成果:
- QM9star含有约190万个离子和激素,以及12万个中性分子.
- 数据包括全面的全球和原子量子化学特性.
- 展示了原子属性分布的原理.
结论:
- QM9star是研究化学中间体的宝贵资源.
- 该数据集将推动机器学习在化学反应性预测方面取得进展.
- 促进了分子表示学习的发展.
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