机器学习预测热膨胀系数的矿氧化物与实验验证验证
Kevin P McGuinness1, Anton O Oliynyk2, Sangjoon Lee3
1SeeO2 Energy Inc., 3655 36 St NW, Calgary, AB T2L 1Y8, Canada. founders@seeo2energy.com.
Physical chemistry chemical physics : PCCP
|November 21, 2023
概括
机器学习模型预测矿氧化物的热膨胀系数 (TEC),对于固体氧化物燃料电池 (SOFC) 和电解电池 (SOEC) 至关重要. 这加速了对新材料的发现,这些新材料具有高温应用所需的性能.
科学领域:
- 材料科学 材料科学 材料科学
- 计算材料科学科学 计算材料科学
- 储能 储能 储能 储能 储能 储能
背景情况:
- 矿氧化物对于固体氧化物燃料电池 (SOFC) 和电解电池 (SOEC) 是至关重要的,因为它们的高温应用.
- 热膨胀系数 (TEC) 是这些设备中矿性能的一个关键性质.
- 合成和测试矿是昂贵和耗时的,而基于物理的模型是计算密集的.
研究的目的:
- 开发一种机器学习 (ML) 模型,用于预测矿氧化物的TEC.
- 应用ML模型来选大量潜在的矿化合物.
- 为了确定用于能源应用的可靠TEC预测的矿化合物.
主要方法:
- 利用机器学习 (ML) 算法来构建TEC的预测模型.
- 在现有的矿数据上训练模型.
- 应用训练的ML模型来预测数百万潜在AA'BB'O3矿化合物的TEC.
主要成果:
- 开发了一种可靠的ML模型来预测矿TEC.
- 为150,451种独特的矿组成生成了TEC预测.
- 成功选了数以百万计的潜在矿成分.
结论:
- 机器学习提供了一种有效的方法来预测材料特性,例如矿的TEC.
- 开发的ML模型加速了用于SOFC和SOEC的新型矿材料的发现.
- 这项工作为大量矿组成提供了有价值的TEC数据,有助于未来的研究和开发.
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