Performs self-consistent mean-field calculations for quantum lattice models in Julia.
npx skills add https://github.com/QuantumBFS/quantum.harness --skill method-mf
Please help me install this Agent Skill. Skill: method-mf Source: https://github.com/QuantumBFS/quantum.harness/tree/main/skills/method-mf Command: npx skills add https://github.com/QuantumBFS/quantum.harness --skill method-mf
This skill provides a rapid, self-consistent baseline for quantum lattice models, allowing researchers to quickly estimate order parameters and phase diagrams without the high computational cost of fully correlated methods.
Run the mean-field self-consistent loop for the specified lattice model using the canonical Julia stack.
High-intent search queries and answers about installing and using this skill.
To perform a self-consistent mean-field calculation, this skill executes a self-consistent-field iteration loop and convergence verification for quantum lattice models. It handles Hartree-Fock for fermions and Weiss decoupling for spins.
Estimating phase diagrams without high computational cost is achieved by identifying candidate broken symmetries and phase boundaries using self-consistent mean-field approximations for quantum lattice models.
Yes, you can generate reference configurations to seed correlated computational methods like DMRG, VMC, or QMC. The skill produces these reference states from self-consistent mean-field decoupling.
Yes, a stable Julia environment is required to execute the self-consistent-field iteration loop and convergence verification. The skill uses the canonical Julia stack to ensure proper calculation stability.
You should use Hartree-Fock mean-field when you need a rapid baseline to estimate order parameters for fermion models without the high computational cost of fully correlated methods. It is ideal for initial phase boundary identification.
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