基于深度学习的双阶段转移学习,用于基于格子导热率的预测
Liudmyla Klochko1, Mathieu d'Aquin1
1Université de Lorraine, LORIA, Nancy F-54000, France. liudmyla.klochko@loria.fr.
Physical chemistry chemical physics : PCCP
|March 13, 2026
概括
转移学习显著增强机器学习模型,用于预测材料特性,如晶格导热率 (LTC). 这种方法提高了模型的精度和稳定性,即使使用有限的高质量数据.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学计算化学
- 机器学习 机器学习
背景情况:
- 机器学习通过预测结构的特性来加速材料发现.
- 对于像格子导热率 (LTC) 这样的特性,有限的精确数据阻碍了模型的准确性和概括性.
- 现有的LTC数据集很小,缺乏物质多样性.
研究的目的:
- 调查转移学习对LTC预测深度学习模型的精度和稳定性的影响.
- 使用有限的数据集,提高材料属性预测模型的准确性和通用性.
主要方法:
- 使用深度学习模型 (ParAIsite) 和转移学习技术.
- 在一个大数据集上预先训练模型,用于形成能量预测.
- 应用了第二个预训练步骤,使用大约LTC值 (AGL工作流) 的中型数据集.
- 在精确的LTC值的小,高质量的数据集上微调两次预训练的模型.
主要成果:
- 在预测LTC的高质量近似值方面取得了显著的改进.
- 通过代转移学习证明了深度学习模型的增强精度和稳定性.
- 成功扫描了材料项目数据库的低热导电性化合物,使用精制的模型.
结论:
- 转移学习,特别是当代应用时,有效地克服了材料属性预测的数据限制.
- 开发的模型显示了加速发现具有所需热性质的材料的前景.
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