High entropy alloys (HEAs) possess a vast compositional space, providing exciting prospects for tailoring material properties yet also presenting challenges in their rational design. Efficiently achieving a well-designed HEA often necessitates the aid of atomistic simulations, which rely on the availability of high-quality interatomic potentials. However, such potentials for most HEA systems are missing due to the complex interatomic interaction. To fundamentally resolve the challenge of the rational design of HEAs, we propose a strategy to build a machine learning (ML) interatomic potential for HEAs and demonstrate this strategy using CrFeCoNiPd as a model material. The fully trained ML model can achieve remarkable prediction precision (>0.92 R) for atomic forces, comparable to the ab initio molecular dynamics (AIMD) simulations. To further validate the accuracy of the ML model, we implement the ML potential for CrFeCoNiPd in parallel molecular dynamics (MD) code. The MD simulations can predict the lattice constant (1 % error) and stacking fault energy (10 % error) of CrFeCoNiPd HEAs with high accuracy compared to experimental results. Through systematic MD simulations, for the first time, we reveal the atomic-scale deformation mechanisms associated with the stacking fault formation and dislocation cross-slips in CrFeCoNiPd HEAs under uniaxial compression, which are consistent with experimental observations. This study can help elucidate the underlying deformation mechanisms that govern the exceptional performance of CrFeCoNiPd HEAs. The strategy to establish ML interatomic potentials could accelerate the rational design of new HEAs with desirable properties.

中文翻译：

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高熵合金的机器学习原子间势

高熵合金（HEA）拥有广阔的成分空间，为定制材料性能提供了令人兴奋的前景，但也对其合理设计提出了挑战。有效地实现精心设计的 HEA 通常需要原子模拟的帮助，而原子模拟依赖于高质量原子间势的可用性。然而，由于复杂的原子间相互作用，大多数 HEA 系统都缺乏这种潜力。为了从根本上解决 HEA 合理设计的挑战，我们提出了一种为 HEA 构建机器学习 (ML) 原子间势的策略，并使用 CrFeCoNiPd 作为模型材料演示了该策略。经过充分训练的 ML 模型可以实现原子力的显着预测精度 (>0.92 R)，与从头算分子动力学 (AIMD) 模拟相当。为了进一步验证 ML 模型的准确性，我们在并行分子动力学 (MD) 代码中实现了 CrFeCoNiPd 的 ML 潜力。与实验结果相比，MD 模拟可以高精度地预测 CrFeCoNiPd HEA 的晶格常数（1% 误差）和堆垛层错能（10% 误差）。通过系统的MD模拟，我们首次揭示了单轴压缩下CrFeCoNiPd HEA中与堆垛层错形成和位错交叉滑移相关的原子尺度变形机制，这与实验观察结果一致。这项研究有助于阐明控制 CrFeCoNiPd HEA 优异性能的潜在变形机制。建立 ML 原子间势的策略可以加速具有理想性能的新型 HEA 的合理设计。