Atomistic simulations provide insights into structure-property relations on an atomic size and length scale, that are complementary to the macroscopic observables that can be obtained from experiments. Quantitative predictions, however, are usually hindered by the need to strike a balance between the accuracy of the calculation of the interatomic potential and the modeling of realistic thermodynamic conditions. Machine-learning techniques make it possible to efficiently approximate the outcome of accurate electronic-structure calculations, that can therefore be combined with extensive thermodynamic sampling. We take elemental nickel as a prototypical material, whose alloys have applications from cryogenic temperatures up to close to their melting point, and use it to demonstrate how a combination of machine-learning models of electronic properties and statistical sampling methods makes it possible to compute accurate finite-temperature properties at an affordable cost. We demonstrate the calculation of a broad array of bulk, interfacial, and defect properties over a temperature range from 100 to 2500 K, modeling also, when needed, the impact of nuclear quantum fluctuations and electronic excitations. The framework we demonstrate here can be easily generalized to more complex alloys and different classes of materials.

中文翻译：

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从量子核到热电子状态的有限温度材料建模

原子模拟提供了对原子大小和长度尺度上的结构－属性关系的洞察力，这些关系是可以从实验中获得的宏观可观察物的补充。然而，定量预测通常因需要在原子间电势的计算精度与实际热力学条件的建模之间取得平衡而受到阻碍。机器学习技术可以有效地近似精确的电子结构计算结果，因此可以与大量的热力学采样相结合。我们以元素镍为原型材料，其合金的应用范围从低温到接近其熔点，并用它来演示如何将电子特性的机器学习模型与统计采样方法结合使用，从而以可承受的成本计算出精确的有限温度特性。我们演示了在从100到2500 K的温度范围内计算大量的体积，界面和缺陷性质的方法，并在需要时建模了核量子涨落和电子激发的影响。我们在此演示的框架可以轻松地推广到更复杂的合金和不同类别的材料。在需要时，受到核量子涨落和电子激发的影响。我们在此演示的框架可以轻松地推广到更复杂的合金和不同类别的材料。在需要时，受到核量子涨落和电子激发的影响。我们在此演示的框架可以轻松地推广到更复杂的合金和不同类别的材料。