论文标题
全浓度空间中Al-Cu-mg合金的准确潜在模型
Accurate Deep Potential model for the Al-Cu-Mg alloy in the full concentration space
论文作者
论文摘要
将第一原理的精度和对势能表面描述（PES）描述的经验效率相结合是哲学家通过原子模拟揭示物质本质的哲学家。由于相关PES的复杂和非线性性质，对于多组分合金系统而言，这尤其具有挑战性。在这项工作中，我们通过采用深层电位（DP），基于神经网络的PES表示PES和DP Generator（DP-GEN），为AL-CU-MG系统开发了准确的PES模型，这是一种并发学习的方案，该方案生成了一组紧凑的ad缩写数据集来进行培训。最终的DP模型给出了与其基本能量和机械性能的各种二元和三元系统的第一原理计算一致的预测，包括形成能量，平衡体积，状态方程，间质能，空置，空位和表面形成能以及弹性模块。广泛的基准表明，DP模型已准备就绪，对于在整个浓度范围内的Al-Cu-MG系统的原子建模很有用。
Combining first-principles accuracy and empirical-potential efficiency for the description of the potential energy surface (PES) is the philosopher's stone for unraveling the nature of matter via atomistic simulation. This has been particularly challenging for multi-component alloy systems due to the complex and non-linear nature of the associated PES. In this work, we develop an accurate PES model for the Al-Cu-Mg system by employing Deep Potential (DP), a neural network based representation of the PES, and DP Generator (DP-GEN), a concurrent-learning scheme that generates a compact set of ab initio data for training. The resulting DP model gives predictions consistent with first-principles calculations for various binary and ternary systems on their fundamental energetic and mechanical properties, including formation energy, equilibrium volume, equation of state, interstitial energy, vacancy and surface formation energy, as well as elastic moduli. Extensive benchmark shows that the DP model is ready and will be useful for atomistic modeling of the Al-Cu-Mg system within the full range of concentration.
