论文标题
学习DFT
Learning DFT
论文作者
论文摘要
我们提出了从密度矩阵重新归一化组计算到通过深度学习来构建密度功能理论的逆向工程kohn-sham电位的扩展。我们没有将机器学习应用于能量功能本身，而是将这些技术应用于Kohn-Sham电位。为此，我们制定了一种训练神经网络的计划，以代表从局部密度到Kohn-Sham电位的映射。最后，我们使用神经网络将仿真尺寸尺寸提高到更大的系统尺寸。
We present an extension of reverse engineered Kohn-Sham potentials from a density matrix renormalization group calculation towards the construction of a density functional theory functional via deep learning. Instead of applying machine learning to the energy functional itself, we apply these techniques to the Kohn-Sham potentials. To this end we develop a scheme to train a neural network to represent the mapping from local densities to Kohn-Sham potentials. Finally, we use the neural network to up-scale the simulation to larger system sizes.
