Atomistic simulations play an important role in elucidating the physical properties of iron at extreme pressure and temperature conditions, which in turn provide crucial insights into the present state and thermal evolution of the earth's and planetary cores. However, simulations face challenges in retaining ab initio accuracy at the simulation size and time scales required to address some of the most important geophysical questions. We used deep-learning methods to develop interatomic models for iron covering pressures from 75–650 GPa and temperatures from 4000–7600 K. The models retain ab initio accuracy while being computationally cost effective. Rigorous validation tests attest their accuracy in large-scale simulations as well as in the presence of extended defects. The models pave the way to the determination of the thermodynamic and rheological properties of iron at extreme conditions with ab initio accuracy.

中文翻译：

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深度学习极端条件下铁的原子间势

原子模拟在阐明铁在极端压力和温度条件下的物理性质方面发挥着重要作用，这反过来又为地球和行星核心的现状和热演化提供了重要的见解。然而，模拟面临着在解决一些最重要的地球物理问题所需的模拟规模和时间尺度上保持从头算精度的挑战。我们使用深度学习方法开发了铁覆盖压力为 75-650 GPa、温度为 4000-7600 K 的原子间模型。这些模型保留了从头算的精度，同时在计算上具有成本效益。严格的验证测试证明了它们在大规模模拟以及存在扩展缺陷的情况下的准确性。这些模型为从头开始准确测定铁在极端条件下的热力学和流变特性铺平了道路。