二元及高熵难熔碳化物温度相关弹性的通用机器学习原子间势基准测试

中文摘要 AI 辅助

准确预测温度相关的弹性对于评估高温条件下的难熔碳化物至关重要，但从头算分子动力学（AIMD）的计算成本限制了对不同成分和温度的系统研究。通用机器学习原子间势（uMLIPs）提供了一种高效的替代方案，但其在该任务中的准确性仍未得到充分验证。在此，我们针对5种二元碳化物和2种高熵（HE）碳化物，在300至1200K的温度范围内，以一致的AIMD参考数据为基准，对9种uMLIPs进行了测试。通过应力-应变分子动力学获得弹性常数及对应的体积模量、剪切模量和杨氏模量，该方法明确采样了热原子运动和仅热膨胀之外的非谐效应。我们分别评估了绝对弹性质和归一化热软化程度。MACE-MH-1的总体平均绝对百分比误差最低，为5.7%；MACE-MH-1和DPA4-Mini对热软化的重现最为准确，平均偏差分别为2.5和2.3个百分点。所有模型普遍低估了刚度，对于大多数模型而言，相对于AIMD参考值更大的平衡体积可能是导致这一趋势的原因。C12表现出最大的模型相关误差。高熵碳化物的描述准确性与二元碳化物相当，表明化学复杂性未带来明显的准确性损失；准确性反而随过渡金属成分变化，其中V族碳化物，尤其是TaC，构成了最大的挑战。这些结果确定了适用于碳化物有限温度弹性的有前景的预训练模型，并表明必须独立评估准确的绝对刚度和热软化程度。

英文摘要

Accurate prediction of temperature-dependent elasticity is important for assessing refractory carbides under high-temperature conditions, but the computational cost of ab initio molecular dynamics (AIMD) limits systematic investigations across compositions and temperatures. Universal machine-learning interatomic potentials (uMLIPs) offer an efficient alternative, yet their accuracy for this task remains insufficiently established. Here, we benchmark nine uMLIPs against consistent AIMD reference data for five binary and two high-entropy (HE) carbides between 300 and 1200 K. Elastic constants and the corresponding bulk, shear, and Young's moduli are obtained using stress-strain molecular dynamics, explicitly sampling thermal atomic motion and anharmonic effects beyond thermal expansion alone. We assess absolute elastic properties and normalized thermal softening separately. MACE-MH-1 achieves the lowest overall mean absolute percentage error (5.7%). MACE-MH-1 and DPA4-Mini reproduce thermal softening most accurately, with mean deviations of 2.5 and 2.3 percentage points, respectively. All models generally underestimate stiffness, with larger equilibrium volumes relative to the AIMD reference likely contributing to this trend for most models. C12 exhibits the largest model-dependent errors. The HE carbides are described with accuracy comparable to that of the binary carbides, indicating no apparent accuracy penalty from chemical complexity. Accuracy instead varies with transition-metal composition, with group-V carbides, particularly TaC, presenting the greatest challenge. These results identify promising pretrained models for finite-temperature elasticity in carbides and show why accurate absolute stiffness and thermal softening must be assessed independently.

发表机构

• Czech Technical University in Prague（布拉格捷克理工大学）
• FZU – Institute of Physics of the Czech Academy of Sciences（捷克科学院物理研究所）

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