IMP Heteropolymer Aging Simulation (Metropolis MC)

Complete pipeline for simulating aging dynamics in the IMP heteropolymer with tunable disorder correlations.

Pipeline

Script	Purpose
imp_aging.py	Metropolis MC simulation, temperature quench, two-time observables
analyze_results.py	Disorder-averaged timescales, aging exponent mu, collapse scores
extended_analysis.py	KWW fitting, MSD/alpha2 analysis, energy/Rg tracking
quantitative_analysis.py	Power-law fits, ensemble separation metrics
robustness_analysis.py	Bootstrap CIs, multi-threshold tau, smoothed t*
create_comparison_figures.py	Overlay figures for iid vs correlated
test_imp_aging.py	47-test suite (energy, disorder, observables, KWW)

Quickstart

python3 imp_aging.py \
  --out output/results.csv \
  --ensemble iid correlated \
  --epsilon 0 3 6 \
  --n_disorder 20 --n_traj 8 \
  --seed 123

python3 analyze_results.py --csv output/results.csv --outdir analysis --mono
python3 extended_analysis.py --csv output/results.csv --outdir analysis/extended
python3 -m pytest test_imp_aging.py -v

Observables

Observable	Description
Q(t_w,t)	Contact overlap (fraction of non-bonded pairs in contact at both t_w and t_w+t)
chi4	N_pairs * (var Q across trajectories) - dynamical heterogeneity
D4	Mean squared change in pairwise distances
MSD	Per-bead mean-square displacement
alpha2	Non-Gaussianity parameter (3/5)*<r^4>/<r^2>^2 - 1
Energy	Total potential energy at each snapshot
Rg	Radius of gyration
n_contacts	Number of non-bonded pairs in contact

Disorder Ensembles

• iid: eta_ij ~ N(0,1) independent
• correlated: eta_ij = kappa * sigma_i * sigma_j + sqrt(1-kappa^2) * xi_ij, where sigma is a Markov chain on {+1,-1} with persistence pi