"""
Skyrmion-Optics Metamaterial Cyclization System (SOMS-CS) v3.0: Chemically-Enhanced Transport.
Simulates photochemical cyclization using Skyrmion-guided optical transport, where the
efficiency of the optical tunneling is now coupled to the *intermediate chemical state*
of the precursor molecule, optimizing transport rates.
Core process steps:
- 1. Circular Dichroism (CD) Acquisition: Identifies current molecular conformation.
- 2. Stochastic Interpretation: Derives metamaterial geometry AND initial chemical conditions.
- 3. Skyrmion Field & Optical Gate Configuration: Configures the magnetic and chiral fields.
- 4. Reaction State & Photonic Tunneling Recalibration: Determines the chemical influence on optical coupling.
- 5. Photochemical Cyclization Execution: The transformation process via guided light.
Requires: numpy, matplotlib (for visualization routines).
Usage:
python metamaterial_dna_rna.py
"""
import math
import random
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from typing import List, Tuple
# -------------------------------
# Skyrmion-Optics Metamaterial Cyclization System (SOMS-CS)
# -------------------------------
class SpectrumSignature:
"""Represents the Circular Dichroism signature of the current conformation."""
def __init__(self, ellipticity: float, absorption_peak: float):
self.ellipticity = ellipticity # Handedness/chirality signal (mdeg)
self.absorption_peak = absorption_peak # Wavelength (nm)
class MetamaterialParams:
"""Parameters defining the graphene-based stabilization scaffold."""
def __init__(self, tensile_stress: float, pore_density: float, confinement_rigidity: float):
self.tensile_stress = tensile_stress
self.pore_density = pore_density
self.confinement_rigidity = confinement_rigidity # How much the scaffold resists structural change
class ReactionState:
"""Parameters defining the chemical environment and molecular preparation."""
def __init__(self, catalyst_activity: float, intermediate_stability: float):
self.catalyst_activity = catalyst_activity # E.g., Lewis acid strength (units)
self.intermediate_stability = intermediate_stability # E.g., Solvent-mediated stabilization (units)
class SkyrmionGateConfig:
"""Parameters for the Tunable Skyrmion Field and the Chiral Optical Gate."""
def __init__(self, magnetic_field: float, skyrmion_density: float, optical_polarization: float):
self.magnetic_field = magnetic_field # Controls Skyrmion size/stability (Tesla)
self.skyrmion_density = skyrmion_density # Density of the magnetic 'quanta' (nm^-2)
self.optical_polarization = optical_polarization # Chiral light input (-1.0 to 1.0)
class PrecursorTarget:
"""Represents the target molecule for cyclization."""
def __init__(self, name: str, ring_structure: str):
self.name = name
self.ring_structure = ring_structure # e.g., "Stero-Precursor", "Macro-Lactam"
# -------------------------------
# Initialization
# -------------------------------
def initialize_system():
print("[INIT] SOMS-CS v3.0: Chemically-Enhanced Transport System initialized.\n")
# -------------------------------
# Circular Dichroism (CD) Spectrum Acquisition
# -------------------------------
def capture_cd_spectrum(target: PrecursorTarget) -> SpectrumSignature:
"""Simulates capturing the CD spectrum to determine current chiral state."""
print(f"[CD-SCAN] Scanning target precursor: {target.name} for {target.ring_structure} cyclization.")
ellipticity = round(random.uniform(-15.0, 15.0), 1)
peak = round(random.uniform(280.0, 320.0), 1)
return SpectrumSignature(ellipticity, peak)
# -------------------------------
# Stochastic Interpretation (Metamaterial & Initial Chemistry Derivation)
# -------------------------------
def interpret_stochastically(sig: SpectrumSignature) -> Tuple[MetamaterialParams, ReactionState]:
"""Interprets the signature to derive stabilizing metamaterial and initial reaction parameters."""
# Metamaterial Derivation (as before, focusing on physical confinement)
rigidity = 1.0 + abs(sig.ellipticity) / 10.0
tensile_stress = sig.absorption_peak / 300.0 * 1.2
pore_density = 0.5 + sig.absorption_peak * sig.ellipticity * 0.0001
meta_params = MetamaterialParams(tensile_stress, pore_density, rigidity)
# Initial Reaction State Derivation (new part)
# High ellipticity (strong conformation) may require lower catalyst activity to prevent side reactions
catalyst_activity = max(1.5 - abs(sig.ellipticity) * 0.05, 0.5)
# Stability correlates with the absorption peak (electronic stability)
intermediate_stability = sig.absorption_peak / 300.0 * 1.5
reaction_state = ReactionState(catalyst_activity, intermediate_stability)
return meta_params, reaction_state
# -------------------------------
# Skyrmion Field & Optical Gate Configuration
# -------------------------------
def derive_skyrmion_gate_geometry(params: MetamaterialParams, sig: SpectrumSignature) -> SkyrmionGateConfig:
"""Derives magnetic field and optical gate parameters."""
magnetic_field = 0.8 + params.confinement_rigidity * 0.3
skyrmion_density = math.log(params.tensile_stress + 1.0) * 5.0
optical_polarization = -sig.ellipticity / 15.0
return SkyrmionGateConfig(magnetic_field, skyrmion_density, optical_polarization)
# -------------------------------
# Reaction State & Photonic Tunneling Recalibration
# -------------------------------
def recalculate_tunneling_states(meta_params: MetamaterialParams, skyrmion_config: SkyrmionGateConfig, reaction_state: ReactionState) -> Tuple[float, float, float]:
"""
Determines optical mode coupling efficiency and transport loss,
introducing CHEMICAL INFLUENCE on coupling.
"""
print("[RECAL] Recalculating Skyrmion-Photonic and Chemical states...")
# Base Coupling Efficiency (Magnetic/Optical)
base_coupling = min(skyrmion_config.skyrmion_density / 8.0, 0.98) * (1.0 - abs(skyrmion_config.optical_polarization) * 0.1)
# Chemical Influence Factor (NEW)
# Stable intermediates and high catalyst activity (within limits) improve electron/charge distribution,
# facilitating photonic mode coupling with the spin-wave guide.
chemical_influence = 1.0 + (reaction_state.catalyst_activity * reaction_state.intermediate_stability) * 0.1
# Combined Coupling Efficiency
combined_coupling = min(base_coupling * chemical_influence, 0.99)
# Transport loss (Physical)
transport_loss = max(0.01 + 1.0 / (meta_params.confinement_rigidity * 10.0), 0.05)
print(f"[RECAL] Chemical Influence Factor: {round(chemical_influence, 3)}")
print(f"[RECAL] Combined Coupling Efficiency: {round(combined_coupling, 3)}")
return combined_coupling, transport_loss, chemical_influence
# -------------------------------
# Skyrmion-Optics Tunneling Efficiency Simulation
# -------------------------------
def simulate_tunneling_dynamics(iteration: int, chemical_influence: float) -> float:
"""Simulates the final efficiency of the light transport, now boosted by chemistry."""
print(f"[SKYOP] Simulating Skyrmion-Optics tunneling efficiency at iteration {iteration}...")
skyrmion_stability = 0.8 + np.sin(iteration * 0.4) * 0.15
# Total effective efficiency (boosted by the chemical factor)
tunneling_efficiency = max(0.9 * skyrmion_stability * chemical_influence - iteration * 0.05, 0.5)
print(f"[SKYOP] Skyrmion Stability: {round(skyrmion_stability, 3)} | Tunneling Efficiency: {round(tunneling_efficiency, 3)}")
return tunneling_efficiency
# -------------------------------
# Photochemical Cyclization Execution
# -------------------------------
def execute_cyclization(config: SkyrmionGateConfig, efficiency: float) -> Tuple[bool, float, float]:
"""Attempts the final cyclization, incorporating tunneling efficiency."""
cyclization_yield = efficiency * random.uniform(0.85, 0.95)
enantiomeric_excess = abs(config.optical_polarization) * random.uniform(0.90, 0.99) * 100.0
if efficiency < 0.75:
print("[ERROR] Optical Tunneling Efficiency too low. Reaction poorly controlled. Cyclization aborted.\n")
return False, 0.0, 0.0
print(f"[SUCCESS] Photochemical cyclization completed via Skyrmion-guided light.")
return True, cyclization_yield, enantiomeric_excess
# -------------------------------
# Adaptive Cyclization Loop
# -------------------------------
def adaptive_cyclization_loop(target: PrecursorTarget) -> Tuple[SkyrmionGateConfig, MetamaterialParams, ReactionState, List[float], List[float], List[float], float, float]:
"""The iterative loop to adjust all parameters (Metamaterial, Skyrmion, and Chemical) for optimal results."""
sig = capture_cd_spectrum(target)
meta_params, reaction_state = interpret_stochastically(sig)
skyrmion_config = derive_skyrmion_gate_geometry(meta_params, sig)
coupling_history = []
loss_history = []
efficiency_history = []
chemical_influence_history = []
final_yield, final_ee = 0.0, 0.0
for i in range(5): # Increased iterations for more adaptation
print(f"[LOOP] Iteration {i+1}: Evaluating Skyrmion-Optics Transport with Chemical Influence...")
combined_coupling, transport_loss, chemical_influence = recalculate_tunneling_states(meta_params, skyrmion_config, reaction_state)
coupling_history.append(combined_coupling)
loss_history.append(transport_loss)
chemical_influence_history.append(chemical_influence)
tunneling_efficiency = simulate_tunneling_dynamics(i, chemical_influence)
efficiency_history.append(tunneling_efficiency)
# --- Adaptive Tuning based on Efficiency Feedback ---
if tunneling_efficiency < 0.85:
# Low efficiency requires aggressive adaptation
print("[ADAPT] Tunneling efficiency low. Prioritizing chemical optimization to boost coupling.")
# TUNE 1: Chemical (Improve intermediate stability to reduce electronic drag)
reaction_state.intermediate_stability *= 1.08
reaction_state.intermediate_stability = np.clip(reaction_state.intermediate_stability, 0.1, 2.5)
# TUNE 2: Magnetic (Increase Skyrmion density for better wave guiding)
skyrmion_config.skyrmion_density *= 1.05
elif combined_coupling < 0.8:
# Low coupling suggests mismatch between optical/chemical/physical states
print("[ADAPT] Low coupling efficiency. Adjusting catalyst for electronic state alignment.")
# TUNE 3: Chemical (Fine-tune catalyst to optimize the electronic state for transport)
reaction_state.catalyst_activity += random.uniform(-0.05, 0.05)
reaction_state.catalyst_activity = np.clip(reaction_state.catalyst_activity, 0.5, 2.0)
# TUNE 4: Physical (Minor adjustment to rigidity to support the new chemical state)
meta_params.confinement_rigidity *= 1.01
skyrmion_config = derive_skyrmion_gate_geometry(meta_params, sig)
success, current_yield, current_ee = execute_cyclization(skyrmion_config, tunneling_efficiency)
if success and current_ee > 95.0 and current_yield > 0.90:
final_yield = current_yield
final_ee = current_ee
print(f"[RESULT] {target.name} successfully cyclized to optimal parameters. Yield: {round(final_yield*100, 1)}%, EE: {round(final_ee, 1)}%.\n")
return skyrmion_config, meta_params, reaction_state, coupling_history, loss_history, efficiency_history, final_yield, final_ee
print("[RETRY] Cyclization not optimal. Retrying...\n")
# Return final best attempt if loop completes without optimal success
return skyrmion_config, meta_params, reaction_state, coupling_history, loss_history, efficiency_history, final_yield, final_ee
# -------------------------------
# Visualization Routines
# -------------------------------
def visualize_tunneling_dynamics(target: PrecursorTarget, efficiency_history: List[float], chemical_influence_history: List[float]):
"""Visualizes the Skyrmion-Optics Tunneling Efficiency, highlighting the chemical boost."""
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
iterations = list(range(1, len(efficiency_history) + 1))
# Skyrmion density history
skyrmion_density = [4.0 + np.sin(i*0.6) * 1.5 + i*0.5 for i in iterations] # Approximate growth over iterations
ax.plot(skyrmion_density[:len(chemical_influence_history)], chemical_influence_history, efficiency_history, marker='o', color='lightgreen')
ax.set_title(f"Transport Rate vs. Chem/Mag Control: {target.name}")
ax.set_xlabel("Skyrmion Density (nm^-2)")
ax.set_ylabel("Chemical Influence Factor")
ax.set_zlabel("Tunneling Efficiency")
plt.tight_layout()
plt.show()
def visualize_assembly_stages(target: PrecursorTarget):
"""ASCII visualization of the SOMS-CS pipeline."""
print(f"\n[ASCII] Chemically-Enhanced Skyrmion-Optics Cyclization Stages for {target.name}")
print("""
+-----------------------------+
| [Open-Chain Precursor Input]|
+-----------------------------+
|
v
+-----------------------------+
| [CD Spectrum (Conformation)]|
+-----------------------------+
|
v
+-----------------------------+
| [Metamaterial & Chemical Setup]|
+-----------------------------+
|
v
+-----------------------------+
| [Skyrmion Field & Gate Setup]|
+-----------------------------+
|
v
+-----------------------------+
| [TUNING: Chemical & Photonic]|
+-----------------------------+
|
v
+-----------------------------+
| [Photochemical Cyclization] |
+-----------------------------+
|
v
+-----------------------------+
| [Cyclized Chiral Product] |
+-----------------------------+
""")
# -------------------------------
# Commentary
# -------------------------------
def commentary(target: PrecursorTarget, config: SkyrmionGateConfig, meta_params: MetamaterialParams,
reaction_state: ReactionState, coupling_history: List[float], loss_history: List[float],
efficiency_history: List[float], final_yield: float, final_ee: float):
"""Provides a summary of the synthesis run."""
final_coupling = coupling_history[-1] if coupling_history else 0.0
final_loss = loss_history[-1] if loss_history else 0.0
final_efficiency = efficiency_history[-1] if efficiency_history else 0.0
chem_influence = (coupling_history[-1] / (min(config.skyrmion_density / 8.0, 0.98) * (1.0 - abs(config.optical_polarization) * 0.1))) if coupling_history else 1.0
print(f"\n⚛️ Chemically-Enhanced Skyrmion-Optics Synthesis Commentary: {target.name}")
print(f"""
The cyclization of '{target.name}' was optimally achieved by coupling the Skyrmion-Optics transport
with a chemically pre-activated precursor state.
Chemical Parameters:
Final Catalyst Activity: {round(reaction_state.catalyst_activity, 3)} units
Final Intermediate Stability: {round(reaction_state.intermediate_stability, 3)} units
Calculated Chemical Influence Boost: {round(chem_influence, 3)}x
Transport Metrics:
Final Skyrmion Tunneling Efficiency: {round(final_efficiency, 3)}
Achieved Optical Coupling Rate: {round(final_coupling, 3)}
Observed Results:
Cyclization Yield: {round(final_yield * 100, 1)}%
Enantiomeric Excess (EE): {round(final_ee, 1)}%
By tuning the chemical environment (Catalyst and Intermediate Stability), the electronic state
of the precursor was modified, which reduced the photonic scattering barrier, effectively boosting
the Skyrmion-mediated optical transport rate. This synergistic magnetochemical control led to
supra-90% yield and high EE.
""")
# -------------------------------
# Main Execution
# -------------------------------
def main():
initialize_system()
targets = [
PrecursorTarget("Chiral Diene-1", "Diels-Alder Ring"),
PrecursorTarget("Polyketide Core", "Macro-Lactam Ring"),
PrecursorTarget("Prostaglandin Precursor", "Five-Membered Ring")
]
for target in targets:
# Note: The adaptive loop now returns 8 values
results = adaptive_cyclization_loop(target)
config, params, reaction_state, coupling_history, loss_history, efficiency_history, final_yield, final_ee = results
visualize_assembly_stages(target)
visualize_tunneling_dynamics(target, efficiency_history, reaction_state.intermediate_stability)
commentary(target, config, params, reaction_state, coupling_history, loss_history, efficiency_history, final_yield, final_ee)
print("[END] SOMS-CS v3.0 pipeline complete.")
if __name__ == "__main__":
main()