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Related Concept Videos

Hückel's Rule Diagram of π MOs: Frost Circle01:08

Hückel's Rule Diagram of π MOs: Frost Circle

The Frost circle or the inscribed polygon method is a graphical method for determining the relative energies of π molecular orbitals (MOs) for planar, fully conjugated, and monocyclic compounds. This method was first described by A. A. Frost and Boris Musulin in 1953.
A Frost circle is constructed by drawing a polygon whose number of edges is equal to the number of carbons of the given cyclic system, with one of the vertices pointing down. Then, a circle is drawn enclosing the polygon so that...
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Valence shell electron-pair repulsion theory (VSEPR theory) enables us to predict the molecular structure around a central atom from an examination of the number of bonds and lone electron pairs in its Lewis structure. The VSEPR model assumes that electron pairs in the valence shell of a central atom will adopt an arrangement that minimizes repulsions between these electron pairs by maximizing the distance between them. The electrons in the valence shell of a central atom form either bonding...
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Conjugated dienes have lower heats of hydrogenation than cumulated and isolated dienes, making them more stable. The enhanced stabilization of conjugated systems can be understood from their π molecular orbitals.
The simplest conjugated diene is 1,3-butadiene: a four-carbon system where each carbon is sp2-hybridized and has an unhybridized p orbital that contains an unpaired electron. According to molecular orbital theory, atomic orbitals combine to form molecular orbitals such that the number...
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Molecules have characteristic shapes that are crucial for their function. The arrangement of various electron groups around the central atom dictates their molecular geometry. Electron pairs in the valence shell of a central atom will adopt an arrangement that minimizes repulsions between the electron pairs by maximizing the distance between them. The valence electrons form either bonding pairs, located primarily between bonded atoms, or lone pairs.
Two regions of electron density in a diatomic...

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Updated: May 14, 2026

Scalable Nanohelices for Predictive Studies and Enhanced 3D Visualization
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Published on: November 12, 2014

π -stack optimizer: framework for the design of one-dimensional supramolecular systems.

Arunima Ghosh1, Barik Susmita1, Roshan J Singh2

  • 1Centre for Computational and Data Sciences, Indian Institute of Technology Kharagpur, Kharagpur, 721302, West Bengal, India.

Journal of Molecular Modeling
|May 13, 2026
PubMed
Summary

The pi-stack optimizer efficiently predicts stable one-dimensional supramolecular assemblies by exploring configurations and identifying low-energy structures. This computational tool aids in generating accurate models for non-covalently bonded systems.

Keywords:
Global optimizationMolecular modelingSupramolecular

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Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

Published on: April 12, 2019

Area of Science:

  • Computational Chemistry
  • Supramolecular Chemistry
  • Materials Science

Background:

  • Predictive modeling of one-dimensional (1D) supramolecular assemblies is challenging due to vast configurational spaces and complex energy landscapes in non-covalently bonded systems.
  • Identifying stable, low-energy configurations is crucial for understanding and designing supramolecular structures.
  • Existing methods often require significant computational resources and expertise.

Purpose of the Study:

  • To introduce the pi-stack optimizer, a novel open-source framework for generating energetically favorable 1D stacking motifs.
  • To enable direct prediction of stable supramolecular assemblies from single monomeric building blocks with minimal computational cost.
  • To provide a scalable and practical tool for generating high-quality initial structures for advanced computational studies.

Main Methods:

  • The pi-stack optimizer employs global optimization algorithms to explore multidimensional parameter spaces, including rigid-body translations, rotations, and intramolecular torsional flexibility.
  • It integrates molecular symmetry constraints to avoid redundant configuration exploration and utilizes metaheuristic algorithms (e.g., Particle Swarm Optimization, Genetic Algorithms) for sampling.
  • Candidate geometries are evaluated using semi-empirical quantum-mechanical calculations (GFN2-xTB) with an objective function combining binding energies and steric penalties.

Main Results:

  • The framework successfully identified stable low-energy configurations across 14 diverse supramolecular systems, including those with directional hydrogen-bonding networks.
  • Comparative analyses showed consistent convergence to similar low-energy minima across different optimization algorithms, demonstrating robustness.
  • Automated hyperparameter optimization via Optuna enhances the framework's efficiency and scalability.

Conclusions:

  • The pi-stack optimizer is a reliable and computationally efficient tool for predicting 1D supramolecular assembly structures.
  • It significantly reduces the overhead associated with generating initial structures for advanced quantum-mechanical calculations and molecular simulations.
  • The open-source, modular design makes it a versatile resource for researchers in computational and supramolecular chemistry.