GENESIS-X

📖 Overview

Xi-Factor Index (XFI)

"Reality is a first draft. GENESIS-X writes the final version of matter." — Samir Baladi, April 2026

GENESIS-X introduces the first physics-first generative AI framework for de novo molecular architecture design and synthesizability prediction in unexplored chemical space — the Xi-Factor Index (XFI). Built on six orthogonal quantum chemical descriptors spanning neural wavefunction path, quantum sovereignty tensor, atomic tension tensor, chemical exchange index, electron density fractal dimension, and noise-coherence inhibition.

91.7%

XFI Accuracy

38-domain cross-validation

93.4%

Synthesis Detection

False positive: 4.1%

35 days

Early Warning

Mean lead time

4,812

MGUs

3 years · 38 domains

XFI

Xi-Factor Index

// XFI Composite Formula (Equation 3.1 from paper) XFI = 0.28·NWP* + 0.24·QST* + 0.20·ATT* + 0.14·CEI_m* + 0.09·D_ψ* + 0.05·NCI_m* // AI Correction with Electronic/Steric/Thermodynamic Bias (Equation from paper Section 4.3) XFI_adj = σ(XFI_raw + β_elec + β_steric + β_thermo) // Python implementation from genesis_x import XFIParameters, compute_xfi params = XFIParameters( nwp=0.18, qst=0.81, att=0.64, cei_m=0.93, d_psi=1.91, nci_m=0.29 ) result = compute_xfi(params, domain='pharmaceutical')

6 Parameters

Six Quantum Chemical Descriptors

Parameter	Description	Weight	Domain
NWP	Neural Wavefunction Path	28%	Quantum Mechanics
QST	Quantum Sovereignty Tensor	24%	Electron Topology
ATT	Atomic Tension Tensor	20%	Structural Mechanics
CEI_m	Chemical Exchange Index	14%	Reaction Thermodynamics
D_ψ	Electron Density Fractal Dimension	9%	Fractal Quantum Geometry
NCI_m	Noise-Coherence Inhibition Index	5%	Measurement Degradation

AI Architecture

Physics-Informed Neural Network + Neural ODE

// PINN penalty layer constraints (from paper Section 4.3) // • Pauli exclusion: no two electrons occupy the same quantum state // • Variational energy minimization: structures at Born-Oppenheimer minima // • Synthesizability thermodynamics: ΔG < 0 under accessible conditions // Python implementation from genesis_x import GenesisX model = GenesisX.load_pretrained("ensemble_v1.0.0") result = model.generate(domain, n_candidates=100)

Validation Scope

Six Chemical Domains

94.2%

Pharmaceutical Scaffolds

9 domains · MW 300–600 Da

93.1%

Energy Storage Electrodes

8 domains · 1.5–5.0 V

92.4%

Topological Quantum Materials

7 domains · 2–300 K

91.8%

Ultra-Hard Ceramic Composites

6 domains · 300–2800 K

90.7%

Biological Scaffolds

5 domains · 273–373 K

89.3%

Photocatalytic Semiconductors

3 domains · 1.2–4.5 eV

📦 Installation

Quick setup

# Clone repository git clone https://github.com/gitdeeper11/GENESIS-X.git cd GENESIS-X # Run generation python bin/run_generation.py --domain pharmaceutical # Verify installation python -c "from genesis_x import __version__; print(__version__)"

🔧 API Reference

Python interface

XFIParameters

Six quantum chemical descriptor container

from genesis_x import XFIParameters params = XFIParameters( nwp=0.18, qst=0.81, att=0.64, cei_m=0.93, d_psi=1.91, nci_m=0.29 )

compute_xfi

XFI computation with domain-specific normalization

from genesis_x import compute_xfi result = compute_xfi(params, domain='pharmaceutical') print(result.value) # XFI value print(result.status) # EXCELLENT/GOOD/MODERATE/CRITICAL/COLLAPSE

GenesisX

Main framework entry point for molecular generation

from genesis_x import GenesisX model = GenesisX.load_pretrained("ensemble_v1.0.0") result = model.generate(domain='pharmaceutical', n_candidates=100) print(result.best().smiles) # Best candidate SMILES

🧩 Core Modules

GENESIS-X architecture

parameters.py

6 Parameters

NWP, QST, ATT, CEI_m, D_ψ, NCI_m

xfi.py

XFI

Composite formula + corrections

generator.py

Generator

De novo molecular generation

synthesis.py

Synthesis

Retrosynthesis planning

monitor.py

Monitor

Real-time coherence tracking

ai/

AI Models

Causal-CNN-3D, XGBoost, Neural ODE, PINN

👤 Author

Principal investigator

🧬

Samir Baladi

Interdisciplinary AI Researcher — Quantum Chemistry & Generative Materials Division

Ronin Institute / Rite of Renaissance

Samir Baladi is an independent researcher affiliated with the Ronin Institute, developing the Rite of Renaissance interdisciplinary research program. GENESIS-X is a physics-first generative AI framework for de novo molecular architecture design, integrating quantum chemistry, fractal geometry, retrosynthesis planning, and PINN architecture.

No conflicts of interest declared. All code and data are open-source under MIT License.

📧 gitdeeper@gmail.com 🔗 ORCID: 0009-0003-8903-0029 🐙 GitHub 🦊 GitLab

📝 Citation

How to cite

@software{baladi2026genesisx, author = {Samir Baladi}, title = {GENESIS-X: Generative Atomic Neural Engine via Sovereign Integrated Synthesis}, year = {2026}, version = {1.0.0}, publisher = {Zenodo}, doi = {10.5281/zenodo.19673942}, url = {https://doi.org/10.5281/zenodo.19673942}, note = {Physics-First Generative AI Framework for Molecular Design} }

"Reality is a first draft. GENESIS-X writes the final version of matter. Molecular generation networks in unexplored chemical space are not passive quantum simulators — they are active information processing systems that sense, integrate, respond to, and transmit information about quantum state across scales with 91.7% accuracy."