利用红外光谱学进行自动化结构阐明
Marvin Alberts1,2,3, Teodoro Laino4,5, Alain C Vaucher4,5
1IBM Research Europe, Rüschlikon, Switzerland. marvin.alberts@ibm.com.
机器学习现在可以直接从红外 (IR) 光谱预测分子结构. 这种变压器模型解锁了全谱数据,改善了化学分析,超出了人类的解释范围.
科学领域:
- 计算化学是一种计算化学.
- 分析化学是一种分析化学.
- 频谱学是一种光谱学.
背景情况:
- 机器学习 (ML) 在化学中的应用正在迅速发展.
- 分析化学已经引起了ML的兴趣,但实际采用有限.
- 红外 (IR) 光谱是可访问的,但由于复杂的峰值解释,通常只能识别有限的功能组.
研究的目的:
- 开发一个变压器模型,从完整的红外光谱中直接预测分子结构.
- 克服人类对红外光谱学解释的局限性.
- 为了利用红外光谱的全部信息内容进行化学分析.
主要方法:
- 一个变压器模型被设计用于IR光谱分析.
- 该模型在一个大数据集上进行了预训练,其中包括634585个模拟的红外光谱.
- 用3453个实验性红外光谱进行了微调.
主要成果:
- 该模型实现了44.4%的top-1和69.8%的top-10准确度,用于预测分子结构 (6-13重原子).
- 对于脚手架预测,该模型实现了84.5%的top-1和93.0%的top-10准确性.
- 证明了利用全面光谱信息的能力.
结论:
- 变压器模型有效地从IR光谱中预测分子结构和支架.
- 这种方法显著提高了红外光谱在化学中的实用性.
- 能够从光谱数据中获得更深入的见解,超出了传统的功能组识别.
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