Molecular dynamics simulations are indispensable for exploring the behavior of atoms and molecules. Grounded in quantum mechanical principles, quantum molecular dynamics provides high predictive power but its computational cost is dominated by iterative high-fidelity electronic structure calculations. We propose a novel model order reduction approach as an alternative to high-fidelity electronic structure calculation. By learning a low-dimensional representation of the electronic solution manifold within the Kohn-Sham density functional theory framework, our model order reduction approach determines the ground state electronic density by projecting the problem onto a low-dimensional subspace, thereby avoiding the computationally expensive iterative optimization of electronic wavefunctions in the full space. We demonstrate the capability of our method on a water molecule, showing excellent agreement with high-fidelity simulations for both molecular geometry and dynamic properties, highlighting the generalizability through carefully designed parametrization and systematic sampling.

翻译：分子动力学模拟对于探索原子和分子的行为是不可或缺的。基于量子力学原理的量子分子动力学具有较高的预测能力，但其计算成本主要来源于迭代的高精度电子结构计算。我们提出了一种新颖的模型降阶方法，以替代高精度电子结构计算。通过在Kohn-Sham密度泛函理论框架内学习电子解流形的低维表示，我们的模型降阶方法通过将问题投影到低维子空间来确定基态电子密度，从而避免了在全空间中对电子波函数进行计算昂贵的迭代优化。我们在水分子上验证了该方法的性能，结果显示其在分子几何结构和动态特性方面与高精度模拟高度吻合，并通过精心设计的参数化和系统采样突出了方法的泛化能力。