AI 中文总结
本研究以Ni-Pd体系为原型，构建高效机器学习势，证实Ni-Pd固态溶液约600 K时热力学稳定，为无序合金研究提供高通量工作流程，可扩展至多组分体系。
AI 中文摘要
预测在广阔成分空间中形成固态溶液的稳定合金是一项严峻的理论挑战，因为必须评估吉布斯自由能，包括组态和振动贡献，这需要能实现极高通量的能量理论。以Ni-Pd体系为原型，我们构建了一种基于密度泛函理论数据的高效雅可比-勒让德机器学习势，其在整个成分空间内提供准确的能量和力。基于至多三体项的集团展开，仅含873个可训练参数，这使我们能够通过直接积分所有可及的微态来计算配分函数，这些微态因成分、原子构型和热扰动而不同。我们证实Ni和Pd完全互溶，形成fcc固态溶液，其在室温下仅为亚稳定，而在约600 K时变为热力学稳定，且稳定性首先出现在成分范围的富Pd端。有趣的是，熵和热容分析显示，该固态溶液与两种具有长程L1₀结构的金属间相（NiPd和NiPd₃）存在竞争关系。总体而言，我们的方法为无序合金的研究提供了强大且高通量的工作流程，该方法可扩展至多组分体系，如高熵合金。
英文摘要
The prediction of stable alloys forming solid-state solutions across large portions of the composition space is a serious theoretical challenge, since one has to evaluate the Gibbs free energy, including both configurational and vibrational contributions. This requires an energy theory capable of extremely high throughput. By taking the Ni-Pd system as prototype, we construct an efficient Jacobi-Legendre machine-learning potential based on density-functional-theory data, which provides accurate energies and forces across the entire composition space. Based on a cluster expansion up to three-body terms and only 873 trainable parameters, this allows us to compute the partition function by directly integrating all accessible microstates, differing for composition, atomic configuration and thermal agitation. We confirm that Ni and Pd are fully miscible, forming an $fcc$ solid-state solution. This is only metastable at room temperature, while becomes thermodynamically stable at around 600~K, with the stability achieved first at the Pd-rich end of the composition range. Interestingly, entropy and heat capacity analysis reveal a competition between the solid-state solution and two intermetallic phases with long-period L1$_0$ structure for NiPd and NiPd$_3$. All in all, our approach offers a powerful and high-throughput workflow for the study of disordered alloys, an approach that can be extended to multi-component systems such as high-entropy alloys.
