Quantum π-Driven Predictive Chemistry: Applications to Reactivity, Electronic Structure, and Simulation-Based Forecasting

I develop the concept of quantum π as a predictive invariant for chemical reactivity, electronic structure, and simulation-based forecasting. Building on my observation that π governs phase continuity, normalization, and standing-wave structure in quantum systems, I extend this framework to molecular chemistry, where π emerges naturally in delocalized electron networks, orbital topology, and reaction pathways. I propose that quantum π can serve as a unifying descriptor linking molecular symmetry, energetic spacing, resonance strength, and transition-state accessibility. Through analytical reasoning and simulation-inspired examples, I show how quantum π can be directly applied to predict stability trends, reaction outcomes, charge redistribution, spectroscopic signatures, and the energetic landscape of reactive intermediates. This article establishes quantum π as a foundational principle for predictive chemistry

Publication Details

Journal
AfricArXiv (OSF Preprints)
Published
2025-11-19
DOI
https://doi.org/10.60763/africarxiv/10320
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Quantum π-Driven Predictive Chemistry: Applications to Reactivity, Electronic Structure, and Simulation-Based Forecasting

AfricArXiv (OSF Preprints)
Machine Learning in Materials Science
article

Quantum π-Driven Predictive Chemistry: Applications to Reactivity, Electronic Structure, and Simulation-Based Forecasting

article en

Abstract

I develop the concept of quantum π as a predictive invariant for chemical reactivity, electronic structure, and simulation-based forecasting. Building on my observation that π governs phase continuity, normalization, and standing-wave structure in quantum systems, I extend this framework to molecular chemistry, where π emerges naturally in delocalized electron networks, orbital topology, and reaction pathways. I propose that quantum π can serve as a unifying descriptor linking molecular symmetry, energetic spacing, resonance strength, and transition-state accessibility. Through analytical reasoning and simulation-inspired examples, I show how quantum π can be directly applied to predict stability trends, reaction outcomes, charge redistribution, spectroscopic signatures, and the energetic landscape of reactive intermediates. This article establishes quantum π as a foundational principle for predictive chemistry

AfricArXiv (OSF Preprints)
Openalex Percentile: Top 56%
Machine Learning in Materials Science
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.