机器学习光谱学使用二阶段的,一般化组成部分贡献协议
Jinming Fan1,2, Chao Qian1,2, Shaodong Zhou1,2
1College of Chemical and Biological Engineering, Zhejiang Provincial Key Laboratory of Advanced Chemical Engineering Manufacture Technology, Zhejiang University, 310027 Hangzhou, P. R. China.
一个结合贝叶斯神经网络和群体贡献方法的新协议使用最小的数据准确地预测分子吸收光谱. 这种方法提高了单个分子和混合物的效率和准确性.
科学领域:
- 计算化学是一种计算化学.
- 在光谱学中的机器学习应用.
背景情况:
- 准确预测分子吸收光谱对于各种化学应用至关重要.
- 现有的机器学习方法通常需要大型数据集来进行可靠的光谱预测.
研究的目的:
- 开发一种高精度和高效的协议,用于预测分子吸收光谱.
- 为了减少对光谱预测模型的数据要求.
主要方法:
- 贝叶斯神经网络 (BNN) 与纠正组贡献 (CGC) 方法的整合.
- 开发一种分子贡献 (MC) 方法来解释CGC的混合规则.
- 使用小型训练数据集 (<100个样本) 进行初始模型训练.
主要成果:
- 准确预测单个分子的最大波长,培训样本少于100个.
- 在<500个样本的全紫外光谱预测中,平均平方误差达到了<2%.
- 使用MC-CGC方法预测混合物的光谱的高精度.
结论:
- 结合的CGC-MC-BNN协议在光谱预测准确性和效率方面取得了重大进展.
- 这种以数据为导向的方法,与化学原理相结合,对更广泛的分子性质预测任务具有前景.
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