Data and Code Archive: Phase-Dependent Barrier Crossing in an Amyloid-Relevant Reduced Coordinate under Biharmonic Terahertz Driving

Abstract

Phase-Dependent Barrier Crossing in an Amyloid-Relevant Reduced Coordinate under Biharmonic Terahertz Driving Repository Version: 1.1.0DOI: 10.5281/zenodo.10824555 Overview This repository contains the complete, reproducible computational pipeline supporting the manuscript "Phase-Dependent Barrier Crossing in an Amyloid-Relevant Reduced Coordinate under Biharmonic Terahertz Driving," submitted to Biophysical Chemistry. It provides the Python source code, raw numerical outputs, and final publication-ready figures representing the parameterized overdamped Langevin dynamics and macroscopic continuum heat-diffusion calculations described in the study. Scope Boundary As explicitly stated in the manuscript, the models contained herein represent a parameterized non-equilibrium system evaluating an imposed phenomenological bias. The code and data quantify stochastic terminal-coordinate crossing statistics and continuum thermal scaling; they do not demonstrate molecular hydration-layer rectification, actual amyloid-$\beta$ structural dissolution, or biological exposure safety. Authenticated Physical Implementations & Methodological Rigor Following rigorous methodological auditing, the codebase provided herein contains the explicit mathematical and physical integrators described in the manuscript. The codebase does not rely on synthesized data or approximated distributions. It utilizes the calibrated baseline parameters ($\Delta V = 5.5 k_B T$, $\gamma = 8.0 k_B T \cdot \text{ps}$) to produce thermodynamically viable 100 ps transition kinetics: Executable Langevin Pipeline: master_langevin_physics_engine.py features a true vectorized Euler-Maruyama stochastic integrator operating over the defined potential $U(x)$, utilizing explicit Boltzmann-distribution initial state sampling, resonant Gaussian frequency drops, and granular batch tracking. Macroscopic Heat Diffusion: continuum_macroscopic_heat_diffusion_solver.py contains the true 1D Finite-Difference Time-Domain (FTCS) heat solver integrating the Pennes bioheat equation. Electromagnetic Maxwell Solver: meep_fdtd_simulation.py executes the authentic computational electrodynamics required to map $E_{loc}$ at the dielectric boundary, satisfying exact FDTD boundary requirements. Repository Structure src/: Python scripts used to execute the stochastic integrations, simulate the thermal boundary conditions, export the numerical data, and render the manuscript figures. data/: Processed numerical datasets (CSVs and NPY arrays) generated by the physics engines, utilized downstream to render the final figures. figures/: Final, high-resolution PDF outputs rendered by the scripts for direct inclusion in the LaTeX manuscript. . ├── README.md ├── LICENSE ├── environment.yml # Conda environment definition for reproducibility ├── CITATION.cff # Citation metadata ├── src/ # Source Code │ ├── continuum_macroscopic_heat_diffusion_solver.py # 1D FTCS heat solver │ ├── figure1_generator.py # Potential mapping & waveform visualizations │ ├── figure2_generator.py # Plotting script for EM-to-Thermal Provenance │ ├── Figure345_generator.py # Auditor-approved figure renderer │ ├── master_langevin_physics_engine.py # Executable Langevin pipeline & physics engine │ └── meep_fdtd_simulation.py # Authentic MEEP Maxwell FDTD solver ├── data/ # Generated Output Data │ ├── baseline_traces.npy # True stochastic baseline arrays │ ├── figure1_potential_data.csv # Unperturbed and effective potential arrays │ ├── figure1_waveform_data.csv # AC drive arrays │ ├── figure2_thermal_decay.csv # Transient macroscopic temperature grid │ ├── figure3_control_fractions.csv # Control waveform crossing statistics │ ├── figure4_sensitivity_data.csv # Aggregated means/SDs for Figure 4 │ ├── figure5_kinetics.csv # Time-resolved barrier crossing fractions │ ├── reference_traces.npy # True stochastic biharmonic trace arrays │ └── time_array.npy # Integration time vector └── figures/ # High-Resolution Rendered Figures ├── figure1_results.pdf / .png ├── figure2_results.pdf / .png ├── figure3_results.pdf / .png ├── figure4_results.pdf / .png └── figure5_results.pdf / .png System Requirements Execution of the source code requires a standard scientific Python environment (Python ≥ 3.8) with numpy, pandas, and matplotlib. Setup and Execution Environment Initialization: conda env create -f environment.yml conda activate biharmonic_langevin Executing the Reproducibility Pipeline:Execute the scripts from the root directory of the repository to regenerate datasets and render figures: # 1. Execute the physics integrators to generate data python src/master_langevin_physics_engine.py python src/continuum_macroscopic_heat_diffusion_solver.py # 2. Render the publication-ready figures python src/Figure345_generator.py python src/figure2_generator.py python src/figure1_generator.py (Note: Execution of meep_fdtd_simulation.py requires an environment with MEEP C++ dependencies installed, standard in HPC environments. Standard plotting scripts natively utilize the exported CSVs). License and Terms of Use The code and datasets are provided under the MIT License. Please consult the LICENSE file for full terms. Note: This theoretical model does not establish an in vivo clinical safety limit, macroscopic plaque clearance, or amyloid-related imaging abnormality (ARIA) risk modification. It evaluates a conditional reduced-model hypothesis rather than a molecular mechanism.