论文标题
在铁磁GDFECO合金中学习相变
Learning phase transitions in ferrimagnetic GdFeCo alloys
论文作者
论文摘要
我们介绍了使用机器学习鉴定铁磁性GDFECO合金中相变的结果。在系统中查找相变的方法是基于“混乱学习”方案，该方案使人们可以使用通用$ W $形状来表征相变。通过应用“混乱学习”方案，我们获得了2D $ W $ -A形状的表面，该表面表征了GDFECO合金的三相过渡点。我们证明我们的结果与热力学潜力的数值最小化的过程完全吻合，但是我们的基于机器学习的方案具有在相变鉴定任务中加速的潜力。
We present results on the identification of phase transitions in ferrimagnetic GdFeCo alloys using machine learning. The approach for finding phase transitions in the system is based on the `learning by confusion' scheme, which allows one to characterize phase transitions using a universal $W$-shape. By applying the `learning by confusion' scheme, we obtain 2D $W$-a shaped surface that characterizes a triple phase transition point of the GdFeCo alloy. We demonstrate that our results are in the perfect agreement with the procedure of the numerical minimization of the thermodynamical potential, yet our machine-learning-based scheme has the potential to provide a speedup in the task of the phase transition identification.
