对177K专利提取的有机分子的IR-NMR多式计算光谱数据集
Federico Zipoli1,2, Marvin Alberts3,4,5, Teodoro Laino3,4
1IBM Research Europe, Saümerstrasse 4, 8803, Rüschlikon, Switzerland. fzi@zurich.ibm.com.
Scientific data
|August 7, 2025
概括
红外 (IR) 和核磁共振 (NMR) 光谱的新合成数据集有助于分子科学. 该资源支持开发准确的预测模型,并加速化学发现工作流程.
科学领域:
- 分子科学 分子科学
- 计算化学计算化学
- 数据科学数据科学数据科学
背景情况:
- 分子科学中的预测建模需要广泛的,高质量的数据集.
- 合成数据生成对于提高模型准确性和加速发现至关重要.
- 现有的光谱数据集可能无法涵盖强大的模型培训所需的多样性.
研究的目的:
- 为有机分子创建一个完整的IR和NMR光谱综合数据集.
- 支持结构阐明和光谱属性预测模型的开发.
- 为基准计算方法和AI模型开发提供一个有价值的资源.
主要方法:
- 混合计算方法集成分子动力学 (MD) 模拟.
- 密度函数理论 (DFT) 对电子结构的计算.
- 机器学习 (ML) 模型用于加速性质预测 (双极时刻).
主要成果:
- 使用ML加速的MD模拟生成了177,461个分子的IR光谱的大数据集.
- 包括1H-NMR和13C-NMR的化学转移为1,255个分子的子集.
- 数据集涵盖了各种各样的有机分子.
结论:
- 合成光谱数据集是科学界的宝贵资源.
- 促进基准测试,计算方法的验证和AI模型增强.
- 有助于解释实验光谱数据,并促进更广泛的利用.
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