分子和材料的平面结构对机器学习模型训练的影响
Luis Cesar de Azevedo1, Ronaldo C Prati1
1Center of Mathematics, Computation and Cognition, Federal University of ABC, Avenida dos Estados, 5001, 09210-580 Santo André, São Paulo, Brazil.
Journal of chemical information and modeling
|August 25, 2023
概括
原子结构中凸体体 (VCH) 的低体积可能会对量子化学数据的机器学习模型训练产生负面影响. 确保训练数据集中的均几何特征对于准确的预测至关重要.
科学领域:
- 计算化学计算化学
- 材料科学 材料科学 材料科学
- 机器学习 机器学习
背景情况:
- 准确的机器学习模型对量子化学 (QC) 数据需要识别阻碍训练的数据特征.
- 之前的工作强调了原子位置的凸船体 (VCH) 的低体积是潜在的问题,导致预测中的异常值.
研究的目的:
- 进一步分析VCH对机器学习模型培训的影响.
- 为了评估VCH的影响,使用带有偏见的采样研究与多样化的分子和材料结构.
主要方法:
- 偏差抽样研究设计.
- 对具有不同VCH的分子和材料结构的分析.
- 基于VCH特征的模型培训绩效的评估.
主要成果:
- 该研究证实,VCH显著影响机器学习模型培训.
- 低VCH值与对模型性能和预测准确性的负面影响有关.
结论:
- 训练数据集中的均几何特征对于可靠的QC属性预测模型至关重要.
- 仔细选择训练数据,考虑VCH,对于构建强大的机器学习模型至关重要.
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