论文标题
深度变化的自由能接近致密氢
Deep Variational Free Energy Approach to Dense Hydrogen
论文作者
论文摘要
我们开发了一种基于基于生成模型的深度变异自由能方法，以实现密集氢的状态。我们采用归一化流网络来对质子玻尔兹曼分布和费米子神经网络进行建模，以在给定的质子位置对电子波函数进行建模。通过共同优化两个神经网络，我们达到了与先前的电子蒙特卡洛计算相当的变异自由能。在行星条件下，密集氢状态的预测方程比从头算分子动力学计算和经验化学模型的发现密集。此外，直接进入密集氢的熵和自由能为行星建模和高压物理学研究开辟了新的机会。
We developed a deep generative model-based variational free energy approach to the equations of state of dense hydrogen. We employ a normalizing flow network to model the proton Boltzmann distribution and a fermionic neural network to model the electron wave function at given proton positions. By jointly optimizing the two neural networks we reached a comparable variational free energy to the previous coupled electron-ion Monte Carlo calculation. The predicted equation of state of dense hydrogen under planetary conditions is denser than the findings of ab initio molecular dynamics calculation and empirical chemical model. Moreover, direct access to the entropy and free energy of dense hydrogen opens new opportunities in planetary modeling and high-pressure physics research.
