论文标题
具有直接力结构的准确且可扩展的多元素图神经网络力场和分子动力学
Accurate and scalable multi-element graph neural network force field and molecular dynamics with direct force architecture
论文作者
论文摘要
最近，机器学习（ML）已用于解决限制了从头算分子动力学（AIMD）的计算成本。在这里，我们提出了GNNFF，这是一种图形神经网络框架，可直接从自动提取的局部原子环境的特征中预测原子力，这些特征是翻译不变的，但在原子的坐标上旋转旋转。我们证明，GNNFF不仅在各种材料系统上的力预测准确性和计算速度方面都能达到高性能，而且还准确地预测了对从较小系统获得的力训练后大型MD系统的力。最后，我们使用我们的框架对超级离子导体进行LI7P3S11的MD模拟，并表明由此产生的LI扩散系数在直接从AIMD获得的li扩散系数范围内。 GNNFF展出的高性能可以很容易地概括以研究其他材料系统的原子水平动力学。
Recently, machine learning (ML) has been used to address the computational cost that has been limiting ab initio molecular dynamics (AIMD). Here, we present GNNFF, a graph neural network framework to directly predict atomic forces from automatically extracted features of the local atomic environment that are translationally-invariant, but rotationally-covariant to the coordinate of the atoms. We demonstrate that GNNFF not only achieves high performance in terms of force prediction accuracy and computational speed on various materials systems, but also accurately predicts the forces of a large MD system after being trained on forces obtained from a smaller system. Finally, we use our framework to perform an MD simulation of Li7P3S11, a superionic conductor, and show that resulting Li diffusion coefficient is within 14% of that obtained directly from AIMD. The high performance exhibited by GNNFF can be easily generalized to study atomistic level dynamics of other material systems.
