qmc

Identify and optimize Quantum Monte Carlo wavefunctions in Pycasino modules.

7|1|Updated Jun 22, 2023
One-click install
npx skills add https://github.com/Konjkov/pycasino --skill qmc
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: qmc
Source: https://github.com/Konjkov/pycasino/tree/main/.claude/skills/qmc
Command: npx skills add https://github.com/Konjkov/pycasino --skill qmc

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Use this skill to work with QMC-related files in the Pycasino project, enabling analysis, optimization, and integration of wavefunction components across modules such as wfn.py, slater.py, jastrow.py, backflow.py, dmc.py, vmc.py, cusp.py, harmonics.py, and ppotential.py.

Core Features & Use Cases

  • Coordinate energy evaluations and wavefunction components (Slater-Jastrow-backflow) for VMC and DMC workflows.
  • Trigger activations on local energy, kinetic energy, drift velocity, branching, and optimization steps to streamline debugging and development.
  • Support optimization methods (varmin, emin, linear method) and parameter handling to tune the wavefunction.

Quick Start

Load the qmc skill to inspect core QMC modules and begin tuning the trial wavefunction using energy calculations within pycasino.

Frequently Asked Questions about qmc

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I optimize a Quantum Monte Carlo wavefunction using a Slater-Jastrow-backflow form?

To optimize a Quantum Monte Carlo wavefunction, you configure the Slater-Jastrow-backflow components within the Pycasino codebase. The skill supports tuning parameters using optimization methods like varmin, emin, and the linear method to minimize energy.

What is the difference between VMC and DMC workflows in Pycasino?

VMC and DMC workflows differ in their sampling algorithms within Pycasino. VMC uses variational sampling with drift velocity, while DMC uses branching and nonlocal potentials for diffusion-based energy calculations.

How do I configure cusp conditions and harmonics for QMC energy calculations?

You configure cusp conditions and harmonics by modifying the cusp.py and harmonics.py modules in Pycasino. This ensures proper wavefunction behavior near particle coalescence during local and kinetic energy evaluations.

Does this skill support debugging local energy and branching steps in DMC?

Yes, the skill supports debugging DMC workflows by triggering activations on local energy, kinetic energy, drift velocity, branching, and optimization steps. This streamlines development and issue isolation within the pycasino environment.

When do I need to apply backflow transformations in a QMC trial wavefunction?

You apply backflow transformations when a basic Slater-Jastrow form lacks sufficient correlation in your QMC trial wavefunction. The skill guides configuration of the backflow.py module to improve variational energy accuracy.

How do I handle nonlocal potentials in QMC simulations?

You handle nonlocal potentials by configuring the ppotential.py module within the Pycasino codebase. The skill provides actionable guidance for integrating these potentials into your VMC and DMC energy calculations.