通过主动学习改进扩散系数预测
Zeno Romero1, Kerstin Münnemann1, Hans Hasse1
1Laboratory of Engineering Thermodynamics, RPTU Kaiserslautern, Erwin-Schrödinger-Str. 44, 67663 Kaiserslautern, Germany.
The journal of physical chemistry. B
|August 29, 2025
概括
积极学习策略有效地指导实验以改善混合物中扩散系数的机器学习预测. 有针对性的测量可以显著提高模型的准确性,而数据的收集也很少.
科学领域:
- 物理化学
- 计算化学
- 化学工程
背景情况:
- 在混合物中预测扩散系数至关重要,但由于数据稀缺,在实验上具有挑战性.
- 机器学习 (ML) 模型具有潜力,但需要大量的训练数据,而这些数据的获取成本很高.
- 积极学习 (AL) 策略可以优化针对性数据采集的实验设计.
研究的目的:
- 研究用于规划扩散系数测量的AL策略.
- 改进基于ML的无限稀释 (D_ij^∞) 的扩散系数预测.
- 评估AL指导数据对矩阵完成方法 (MCM) 的影响.
主要方法:
- 在合成数据上对AL策略进行系统测试.
- 使用不确定性抽样作为有效的AL策略.
- 进行脉冲场梯度 (PFG) 核磁共振 (NMR) 光谱测量,以进行新的Dj∞测量.
- 用新获得的实验数据重新训练混合MCM.
主要成果:
- 不确定性抽样对于规划Dj^∞测量是有效的.
- 测量了19个新的D_ij^∞数据点,用于以前未进行表征的混合物.
- 结合半实证模型 (SEGWE) 的混合MCM预测显示了实质性的准确性改善.
- 测试组的相对平均二次误差几乎减少了一半.
结论:
- 通过最少的实验,AL策略可以显著改善ML对扩散系数的预测.
- AL的有效性取决于具体的ML模型及其与先前信息的整合.
- 有针对性的实验设计是最大限度地提高ML在预测物理属性的关键.
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