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在材料发现中基于分类检测和量化跨领域数据偏差的分类检测和量化

Giovanni Trezza¹, Eliodoro Chiavazzo¹

¹Department of Energy, Politecnico di Torino, C.so Duca degli Abruzzi 24, Torino 10129, Italy.

Journal of chemical information and modeling

|December 16, 2024

概括

这项研究引入了一种新的方法来检测和量化人工智能 (AI) 模型中的数据偏差,通过排除不可靠的现成样本来改善材料发现的预测.

科学领域:

材料科学材料科学材料科学
数据科学数据科学数据科学
人工智能的人工智能

背景情况:

精确的人工智能 (AI) 驱动模型在很大程度上依赖于数据的数量和质量.
数据库生成过程中样本选择中的偏见可能会导致不可靠的预测,当AI模型应用于不同来源的数据时.
这种偏见可能会阻碍基于人工智能的材料发现,即使是大型,高质量的数据集.

研究的目的:

开发一种用于检测和量化AI模型中的数据偏差的方法.
为了减少数据偏差对材料发现的影响.
为了提高AI模型对多样化和未见材料的可靠性.

主要方法:

使用分类策略来识别和量化数据偏差.
该方法识别了"开箱即用"材料,预先训练的模型预测可能不可靠.
该方法使用超导体和热电材料进行验证.

主要成果:

拟议的方法有效地检测和量化数据偏差.
它成功地识别了培训数据分布之外的材料,其预测是不可靠的.
该方法提高了材料发现人工智能模型的可靠性.

结论:

开发的方法为人工智能中偏差检测提供了一个简单,灵活和可适应的解决方案.
它提高了应用于新材料的AI模型的可靠性,推动了AI驱动的材料发现.
这种方法与各种AI架构兼容,包括图等价神经网络.

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