qsGW准粒子和GW-BSE激发能量为133885个分子
Dario Baum1, Arno Förster1, Lucas Visscher2
1Vrije Universiteit Amsterdam, Department of Chemistry and Pharmaceutical Sciences, De Boelelaan 1108, 1081 HZ, Amsterdam, The Netherlands.
Scientific data
|March 11, 2026
概括
一个新的数据集,QM9GWBSE,为分子提供了广泛的准粒子自相一致的GW (qsGW) 和Bethe-Salpeter方程 (BSE) 数据. 本资源旨在推进机器学习模型,用于预测分子激发状态属性.
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.
- 在化学科学中的机器学习.
背景情况:
- 化学科学中的机器学习需要大量高质量的数据集.
- 准粒子自相一致的GW (qsGW) 和贝特-萨尔佩特方程 (BSE) 方法准确地模拟激发状态属性.
- 目前还缺少用于qsGW-BSE计算的全面数据集.
研究的目的:
- 介绍一个大规模的数据集的qsGW-BSE激发能量和qsGW准粒子能量.
- 为训练计算机化学中的先进机器学习模型提供数据.
- 为了方便准确预测分子激发状态属性.
主要方法:
- 生成一个包含qsGW-BSE单元-单元和单元-三元激发能量的数据集.
- 计算相应的过渡双极时刻和振荡器强度.
- 从QM9数据集中包含所有分子的qsGW准粒子能量.
主要成果:
- 介绍QM9GWBSE数据集,这是同类中最大的数据集.
- 数据集包括激发能,过渡二极点时刻,振荡器强度和准粒子能.
- 这些数据涵盖了广泛使用的QM9数据集中的所有分子.
结论:
- QM9GWBSE数据集是化学科学界的宝贵资源.
- 它将作为开发高精度机器学习模型的基础.
- 这一数据集有望显著改善对分子激发状态属性的预测.
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