A simple constrained machine learning model for predicting high-pressure-hydrogen-compressor materials†,Molecular Systems Design & Engineering

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A simple constrained machine learning model for predicting high-pressure-hydrogen-compressor materials†
Molecular Systems Design & Engineering ( IF 3.2 ) Pub Date : 2018-04-30 00:00:00 , DOI: 10.1039/c8me00005k
Jason R. Hattrick-Simpers _{1,

2,

3,

4} , Kamal Choudhary _{1,

2,

3,

4} , Claudio Corgnale _{4,

5,

6}

Affiliation

Here we present the results of using techno-economic analysis as constraints for machine learning guided studies of new metal hydride materials. Using existing databases for hydrogen storage alloys, a regression model to predict the enthalpy of hydrogenation was generated with a mean absolute error of 8.56 kJ mol⁻¹ and a mean relative error of 28%. Model predictions for new hydride materials were constrained by techno-economic analysis and used to identify 6110 potential alloys matching the criteria required for hydrogen compressors. Additional constraints such as alloy cost, composition, and likely structure were used to reduce the number of possible alloys for experimental verification to less than 400. Finally, expert heuristics and a novel machine learning approach to approximating alloy stability were employed to select an Fe–Mn–Ti–X alloy system for future experimental studies.

中文翻译：

用于预测高压氢压缩机材料的简单受限机器学习模型†

在这里，我们介绍使用技术经济分析作为新金属氢化物材料的机器学习指导研究的约束条件的结果。使用现有的储氢合金数据库，生成了预测氢化焓的回归模型，平均绝对误差为8.56 kJ mol ^-1平均相对误差为28％。新氢化物材料的模型预测受到技术经济分析的约束，并用于识别与氢压缩机所需标准相匹配的6110种潜在合金。使用诸如合金成本，成分和可能的结构等其他约束条件，将用于实验验证的可能的合金数量减少到不足400种。最后，采用专家启发法和新颖的机器学习方法来近似合金的稳定性来选择Fe– Mn–Ti–X合金系统，用于未来的实验研究。

更新日期：2018-04-30

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