QMC-DFT-Python is a lightweight open-source code for scientists and enthusiasts to understand the basic concepts behind the implementations of most popular electronic structure calculation method: Density Functional Theory (DFT) and also quantum Monte carlo (QMC).

qmc 的优势主要在于它可以求解强关联系统波函数，这是 dft 无法做到的。 但是由于 QMC 方法的复杂度随着体系的规模呈指数式上升，所以在大体系的求解上自然没有 DFT 来的常用。

量子蒙特卡洛(quantum Monte Carlo，QMC)方法是研究强关联多体问题的一种十分具有说服力的数值模拟方法，它通过随机抽样的办法，对体系的配分函数进行精确的模拟，在许多的强关联模型(例如量子自旋模型，玻色体系)的研究中，体现出了强大的威力。

What is DFT used for in QMC? • DFT is used to relax ground state structures, since QMC relaxation is not yet tractable for crystals. • DFT is used to compute phonons to obtain quasiharmonic estimates of zero point and thermal contributions to the free energy. • DFT is used to generate trial wavefunctions for QMC.

AlSalmi-DFT-QMC-Python is your open-source gateway to understanding the fundamental concepts behind two powerful electronic structure calculation methods: Density Functional Theory (DFT) and Quantum Monte Carlo (QMC). This lightweight code is designed for accessibility and clarity, making it the perfect tool to:

在开发新的泛函的时候，据我所知一些思路就是采用原子的高精度qmc计算结果，进行拟合得到一些基础泛函，进一步再应用到固体超胞、复杂分子中去。

In this work, we designed and applied a workflow that combines DFT+U, QMC (VMC, DMC), and analytical models to estimate a statistical bound for the critical temperature of a 2D magnetic system. Such a workflow is intended to be integrated into the JARVIS framework.

2025年2月13日 · 辅助场量子蒙特卡洛（AFQMC）：结合 密度泛函理论 （DFT）的初始猜测，显著降低采样方差，2022年斯坦福团队借此模拟了含500个电子的二维材料体系。 异构计算加速：GPU集群与张量核架构使QMC采样速度提升百倍，例如2023年谷歌团队在 TPU 上实现每秒千万次量子行走采样。 2. 与机器学习的深度联姻. 采用变分自编码器（VAE）生成低方差采样路径，使费米子系统的收敛速度提升10倍（参见2021年《Nature Machine Intelligence》）。 生成对抗 …

基于杂化展开的连续时间量子蒙特卡洛Hybridization expansion ct-QMC (CT-HYB)是最主要的量子杂质模型求解器,但是... Need a complementary quantum impurity solver! 主流的DFT+DMFT 程序通常与FLAPW 程序(WIEN2K) 对接,但是... DFT+DMFT 程序! The...

Educational code for Density Functional Theory and Quantum Monte Carlo with Python - kayahans/qmc-dft-python