评估基于不确定性的积极学习,以加速分子性质预测的概括
Tianzhixi Yin1, Gihan Panapitiya2, Elizabeth D Coda2,3
1Pacific Northwest National Laboratory, 902 Battelle Blvd, Richland, WA, USA. tianzhixi.yin@pnnl.gov.
不确定性引导的积极学习通过减少数据需求和提高深度学习模型在分子性质预测中的概括性来加速材料发现. 这种方法有助于设计出更好的电解质.
科学领域:
- 计算化学和材料科学计算化学和材料科学
- 机器学习在分子设计中的应用.
背景情况:
- 深度学习模型擅长预测药物设计和材料科学的分子性质.
- 训练这些模型需要广泛的,资源密集的实验数据,限制它们的概括到新的分子结构.
研究的目的:
- 评估不确定性量化方法,通过不确定性引导的实验设计加速材料开发.
- 评估这些方法对电解质设计的有效性,重点关注水溶性和氧化还原潜力的预测.
- 开发新的评估策略,以评估各种数据集的不确定性估计.
主要方法:
- 对现有的不确定性量化技术进行全面评估.
- 开发新的方法来测试在域内和域外数据上的不确定性估计实用性.
- 在实验设计的积极学习框架内应用选定的不确定性估计方法.
主要成果:
- 证明了不确定性引导主动学习的潜力,以减少对分子性质预测的数据要求.
- 展示了经过不确定性引导方法训练的模型改进的概括能力.
- 确定了用于电解质设计应用的有效不确定性估计方法.
结论:
- 不确定性引导的实验设计提供了一种有前途的策略,可以加速发现新材料,特别是电解质.
- 由强大的不确定性量化驱动的积极学习可以显著提高分子设计的效率和范围.
- 开发的评估方法为评估材料科学中不确定性估计的实际实用性提供了一个框架.
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