论文标题
在地球外部核心条件下对Fe-Si-O系统的原子模拟的深度机器学习潜力
A deep machine learning potential for atomistic simulation of Fe-Si-O systems under Earth's outer core conditions
论文作者
论文摘要
使用人工神经网络的机器学习（ANN-ML）来产生原子间电位，已被证明是一种有希望的方法，可以解决长期存在的分子动力学（MD）模拟的准确性与效率的挑战。在这里，以Fe-Si-O系统为原型，我们表明可以开发准确且可转移的ANN-ML电位，以在地球外部芯的高压和高温条件下对材料的可靠模拟进行可靠的MD模拟。 Fe-Si-O系统的ANN-ML电位通过与相关二进制的能量和力拟合在高压和基于密度功能理论（DFT）的第一原理计算获得的高压和温度下的训练。我们表明，生成的ANN-ML电位很好地描述了该复杂系统的液相的结构和动力学。具有DFT精度的有效ANN-ML电势为地球外核中复杂的Fe-Si-O系统的结构和动力学的精确原子模拟提供了有希望的方案。
Using artificial neural-network machine learning (ANN-ML) to generate interatomic potentials has been demonstrated to be a promising approach to address the long-standing challenge of accuracy versus efficiency in molecular dynamics (MD) simulations. Here, taking the Fe-Si-O system as a prototype, we show that accurate and transferable ANN-ML potentials can be developed for reliable MD simulations of materials at high-pressure and high-temperature conditions of the Earth's outer core. The ANN-ML potential for Fe-Si-O system is trained by fitting to the energies and forces of related binaries and ternary liquid structures at high pressures and temperatures obtained by first-principles calculations based on density functional theory (DFT). We show that the generated ANN-ML potential describes well the structure and dynamics of liquid phases of this complex system. The efficient ANN-ML potential with DFT accuracy provides a promising scheme for accurate atomistic simulations of structures and dynamics of complex Fe-Si-O system in the Earth's outer core.
