论文标题
Pareto前部分析和对（R，Z）（Fe，Co，Ti）12（r = y，nd，sm; z = zr，dy）的多目标贝叶斯优化
Pareto front analysis and multi-objective Bayesian optimization for (R, Z)(Fe,Co,Ti)12 (R = Y, Nd, Sm; Z = Zr, Dy)
论文作者
论文摘要
我们提出了一种使用多目标贝叶斯优化（MBO）研究目标变量之间相关性和权衡的方案。我们讨论了Thmn12型化合物的Pareto Front（PF），（R，Z）（Fe，Co，Ti）12（R = Y，ND，SM; Z = Zr，Zr，Dy）在Magne-Timization，Curie温度和使用First-Principles计算中的数据指数方面，我们提取分析的数据。我们表明，权衡关系可用于通过使用部分最小二乘回归来确定可控变量的变化。例如，低成本和高居里温度的趋势与CO的DY和CO降低有关。我们还讨论了MBO作为获得PF特征的实用方案的效率。我们表明，即使获得真实的PF很难，MBO也可以为PF提供近似的集合。
We propose a scheme for investigating the correlation and trade-off among target variables using a multi-objective Bayesian optimization (MBO). We discuss the features of the Pareto front (PF) of ThMn12-type compounds, (R, Z)(Fe,Co,Ti)12 (R = Y, Nd, Sm; Z = Zr, Dy) in terms of magne- tization, Curie temperature, and a price index by using data from first-principles calculations, and we extract the trade-off relations from the analysis. We show that the trade-off relationships can be used to determine changes in the controllable variables by using partial least squares regression. For example, the tendency toward low cost and high Curie temperature is related to the reduction in Dy and increase in Co. We also discuss the efficiency of MBO as a practical scheme to obtain the features of the PF. We show that MBO can offer an approximated set for the PF even when obtaining the true PF is difficult.
