Mat Dft Lobster

mat-dft-lobster

Goal

To calculate advanced chemical bonding properties—like Crystal Orbital Hamilton Populations (COHP), atomic charges, projected DOS, and bonding integrands (ICOHP)—by projecting converged plane-wave Density Functional Theory (DFT) wavefunctions onto a localized, atomic-like basis set using the LOBSTER code.

Background

Standard plane-wave DFT (e.g., VASP) distributes electron density uniformly across reciprocal space, which is computationally robust but lacks explicit chemical intuition regarding localized bonds. LOBSTER (Local Orbital Basis Suite Towards Electronic-Structure Reconstruction) takes the massive WAVECAR from VASP and projects it back to an atomic orbital basis to recover classical chemical bonding insights.

Because WAVECAR files are extremely large (often tens or hundreds of gigabytes), LOBSTER analysis must be performed on the same remote node directly after the VASP static loop. The atomate2 VaspLobsterMaker automates this sequentially (Relax -> Static -> Lobster) and ensures the massive WAVECAR is deleted once the projection completes.

Installation

LOBSTER is free to download for non-commercial use from http://www.cohp.de/.

To use this skill, deploy the compiled lobster binary to your remote HPC worker or local testing environment and ensure its path is exported in your environment PATH. All required Python packages (lobsterpy, ijson) are already provided by the atomate2-agent environment.

Instructions

1. Generate and Execute the Workflow

To submit a LOBSTER workflow, utilize the built-in MCP tool. This natively maps the VaspLobsterMaker directed acyclic graph (DAG) to your HPC resources:

Tool: mcp_atomate2_run_atomate2_vasp_calculation Arguments:

• structures_path: Path to your POSCAR or CIF.
• calculation_type: "lobster"
• execution_mode: "remote" (to execute on the HPC worker)

CRITICAL: Do not run this locally unless you are purely generating testing DAGs (check_only=True). The generated flow contains heavy VASP iterations and high-memory LOBSTER matrix projections.

2. Parse and Analyze Output

Once completed, the termination node returns a LobsterTaskDocument. The most critical file generated is COHPCAR.lobster, which contains the Crystal Orbital Hamilton Populations (COHP).

To analyze COHP outputs, the standard package is LobsterPy. It offers both CLI and Python API tools:

Via CLI:

# Env: atomate2-agent
lobsterpy automatic-plot

Via Python API: Use the provided analyze_lobster.py script as a baseline to parse and visualize the COHPCAR out of the compute node limits.

You can test the DAG generation by running the MCP tool with check_only=True on a structure, or if testing scripts manually:

# Env: atomate2-agent
cd .agents/skills/mat-dft-lobster/examples/GaAs
python ../../scripts/generate_inputs.py --output gaas_flow.json

To plot a sample COHPCAR:

# Env: atomate2-agent
python .agents/skills/mat-dft-lobster/scripts/analyze_lobster.py --cohpcar COHPCAR.lobster --poscar POSCAR --save cohp_plot.png

Constraints

• Environments: Scripts require the atomate2-agent environment.
• HPC Execution: You must map this flow to run on an HPC environment natively since WAVECAR sizes exceed optimal transfer limits. Ensure both vasp_std and lobster binaries are available to the workers.
• Basis Sets: The VaspLobsterMaker optimally restricts VASP settings (e.g., setting ISYM=-1, generating all $k$-points explicitly) to comply with LOBSTER's mathematical constraints. Do not manually override these strict geometry settings unless required by standard pseudopotential edge cases.

References

• Maintz, S., Deringer, V. L., Tchougréeff, A. L., & Dronskowski, R. "LOBSTER: A tool to extract chemical bonding from plane-wave based DFT", J. Comput. Chem., 37, 1030-1035 (2016). DOI

Author: Bowen Deng Contact: GitHub