We created a computational workflow to analyze the potential energy surface (PES) of materials using machine-learned interatomic potentials in conjunction with the minima hopping algorithm. We demonstrate this method by producing a versatile machine-learned interatomic potential for iron hydride via a neural network using an iterative training process to explore its energy landscape under different pressures. To evaluate the accuracy and comprehend the intricacies of the PES, we conducted comprehensive crystal structure predictions using our neural network-based potential paired with the minima hopping approach. The predictions spanned pressures ranging from ambient to 100 GPa. Our results reproduce the experimentally verified global minimum structures such as dhcp, hcp, and fcc, corroborating previous findings. Furthermore, our in-depth exploration of the iron hydride PES at different pressures has revealed complex alterations and stacking faults in these phases, leading to the identification of several different low-enthalpy structures. This investigation has not only confirmed the presence of regions of established FeH configurations but has also highlighted the efficacy of using data-driven, extensive structure prediction methods to uncover the multifaceted PES of materials.

中文翻译：

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机器学习驱动的氢化铁结构预测

我们创建了一个计算工作流程，使用机器学习的原子间势与最小跳跃算法来分析材料的势能表面 (PES)。我们通过神经网络使用迭代训练过程探索不同压力下的能量景观，产生多功能机器学习的氢化铁原子间势，从而演示了这种方法。为了评估 PES 的准确性并理解其复杂性，我们使用基于神经网络的电势与最小跳跃方法相结合进行了全面的晶体结构预测。预测的压力范围从环境压力到 100 GPa。我们的结果重现了经过实验验证的全局最小结构，例如dhcp、hcp和fcc，证实了之前的发现。此外，我们对不同压力下的氢化铁PES的深入探索揭示了这些相中复杂的蚀变和堆垛层错，从而识别出几种不同的低焓结构。这项研究不仅证实了已建立的 FeH 配置区域的存在，而且还强调了使用数据驱动的广泛结构预测方法来揭示材料的多方面 PES 的有效性。