fix: simple hypothetical potential

[n0w0f]

Summary by Sourcery

New Features:

• Introduce a new RASP-compatible geometric potential function using attention-like operations for calculating potential energy.

[sourcery-ai]

Reviewer's Guide by Sourcery

This PR introduces a new RASP-compatible geometric potential function that uses attention-like operations to compute potential energy. The implementation avoids complex mathematical operations and relies on simple arithmetic and comparisons, making it suitable for RASP architecture. The function is integrated into the existing geometry potential system with appropriate coordinate transformations.

Class diagram for the new RASP-compatible geometric potential

classDiagram
    class xtal2pot {
        +lennard_jones(r: float, epsilon: float, sigma: float) float
        +rasp_geometric_potential(x_coords, y_coords, z_coords) float
        +geometry_potential(struct, interaction_order: int, potential: callable) float
    }
    note for rasp_geometric_potential "New function using attention-like operations"
    note for geometry_potential "Updated to integrate rasp_geometric_potential"

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File-Level Changes

Change	Details	Files
Implementation of a new RASP-compatible geometric potential function	Implements attention-like mechanism through kqv function using simple matrix operations Defines distance-based pair energy using discrete bins and Manhattan distance Computes pairwise energies using attention-like operations Aggregates energies using max-like attention mechanism	src/mattext/analysis/xtal2pot.py
Integration of RASP potential into existing geometry potential framework	Adds coordinate transformation from fractional to integer coordinates Implements special case handling for RASP potential in geometry_potential function Registers new potential function in _GEOMETRY_POTENTIALS	src/mattext/analysis/xtal2pot.py

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[sourcery-ai]

Hey @n0w0f - I've reviewed your changes - here's some feedback:

Overall Comments:

• The rasp_geometric_potential function needs more comprehensive documentation explaining: 1) The attention-like algorithm and why it was chosen 2) The reasoning behind the distance binning values 3) Why Manhattan distance is used instead of Euclidean 4) The expected performance characteristics

Here's what I looked at during the review

• 🟡 General issues: 1 issue found
• 🟢 Security: all looks good
• 🟢 Testing: all looks good
• 🟡 Complexity: 1 issue found
• 🟢 Documentation: all looks good

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[sourcery-ai]

suggestion: Add guard against empty input coordinates to prevent IndexError

Consider adding a check for empty input coordinates or ensuring that final_energy is never empty before indexing.

Suggested change

	return final_energy[0]
	if not final_energy:
	raise ValueError("No energy values found in final_energy")
	return final_energy[0]

[sourcery-ai]

issue (complexity): Consider simplifying the geometric potential calculation by using direct matrix operations instead of nested attention-like mechanisms

The current implementation adds unnecessary complexity through nested attention-like operations. Here's a simpler approach that maintains RASP compatibility:

def rasp_geometric_potential(x_coords, y_coords, z_coords):
    """RASP-compatible geometric potential using simplified matrix operations."""
    n = len(x_coords)

    # Compute pairwise Manhattan distances directly
    distances = np.zeros((n, n), dtype=np.int8)
    for i in range(n):
        distances[i] = (abs(x_coords[i] - x_coords) + 
                       abs(y_coords[i] - y_coords) + 
                       abs(z_coords[i] - z_coords))

    # Map distances to energy values using vectorized operations
    energy_map = np.array([8, 4, -2, -1, 0, 0])  # Energy values for bins 0-5
    energies = np.where(distances < len(energy_map), 
                       energy_map[distances], 
                       0)

    # Sum upper triangle to avoid double counting
    return np.sum(np.triu(energies, k=1))

This simplification:

1. Removes the artificial attention abstraction while keeping RASP compatibility
2. Uses direct matrix operations instead of nested kqv functions
3. Simplifies energy calculation with vectorized operations
4. Maintains the same functionality and distance binning

The code is now more maintainable while preserving the required integer-based calculations for RASP compatibility.

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