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Atomic Orbitals

Atomic Orbitals

An atomic orbital represents the three-dimensional regions in an atom where an electron has the highest probability to reside. The radial distribution function indicates the total probability of finding an electron within the thin shell at a distance r from the nucleus. The atomic orbitals have distinct shapes which are determined by l, the angular momentum quantum number. The orbitals are often drawn with a boundary surface, enclosing densest regions of the cloud.

Atomic Absorption Spectroscopy: Atomization Methods

Atomic Absorption Spectroscopy: Atomization Methods

Atomic Absorption Spectroscopy (AAS) atomizes samples through flame atomization or electrothermal atomization. Flame atomization typically involves a nebulizer and spray chamber assembly to combine the sample with a fuel–oxidant mixture, creating a fine aerosol mist that enters a burner. Typically, the fuel and oxidant are combined in an approximately stoichiometric ratio. However, for atoms that are easily oxidized, a fuel-rich mixture may be more advantageous. Only about 5% of the...

Hybridization of Atomic Orbitals II

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Hybridization of Atomic Orbitals I

Hybridization of Atomic Orbitals I

The mathematical expression known as the wave function, ψ, contains information about each orbital and the wavelike properties of electrons in an isolated atom. When atoms are bound together in a molecule, the wave functions combine to produce new mathematical descriptions that have different shapes. This process of combining the wave functions for atomic orbitals is called hybridization and is mathematically accomplished by the linear combination of atomic orbitals. The new orbitals that...

Molecular Models

Molecular Models

Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.

Molecular and Ionic Solids

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Crystalline solids are divided into four types: molecular, ionic, metallic, and covalent network based on the type of constituent units and their interparticle interactions.
Molecular Solids
Molecular crystalline solids, such as ice, sucrose (table sugar), and iodine, are solids that are composed of neutral molecules as their constituent units. These molecules are held together by weak intermolecular forces such as London dispersion forces, dipole-dipole interactions, or hydrogen bonds, which...

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Updated: May 2, 2026

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

GAEAM: a new package for optimizing the embedded atom method potentials of solids by using the genetic algorithm.

Yang-Yang Zhang^1,2, Yu Cheng^1,2, Shu-Wen Zhang^1,2

¹Fundamental Science Center of Rare Earths, Ganjiang Innovation Academy, Chinese Academy of Sciences, Ganzhou, 341000 Jiangxi, China. yyzhang@gia.cas.cn.

Physical Chemistry Chemical Physics : PCCP

|May 1, 2026

Summary

This summary is machine-generated.

GAEAM optimizes embedded atom method (EAM) potentials for solids and alloys using a genetic algorithm. This novel package efficiently reproduces structural and dynamic properties in molecular dynamics simulations.

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Area of Science:

Computational Materials Science
Condensed Matter Physics
Materials Chemistry

Background:

The embedded atom method (EAM) is crucial for molecular dynamics (MD) simulations of solids and alloys.
Optimizing EAM potential parameters presents a significant global optimization challenge for traditional methods.

Purpose of the Study:

Introduce GAEAM, a new package for optimizing EAM potentials using genetic algorithms (GA) and global optimization.
Validate GAEAM's efficacy on five representative alloy systems.

Main Methods:

Developed GAEAM package integrating GA for EAM potential optimization.
Conducted 1.0 µs MD simulations using GAEAM-optimized potentials.
Analyzed radial distribution functions (RDFs), coordination numbers (CN), root-mean-squared displacements (RMSD), and energy evolution.

Main Results:

GAEAM-optimized EAM potentials accurately reproduced structural properties (RDFs, CN) of tested alloys.
Dynamic properties (RMSD, energy evolution) were reliably captured by the optimized potentials.
GAEAM demonstrated robustness and efficiency across diverse alloy systems.

Conclusions:

GAEAM offers a powerful and efficient tool for EAM potential optimization in materials science.
The package streamlines parameter tuning, accelerating research in computational materials science.
GAEAM is extensible to various solid and alloy systems, enhancing MD simulation capabilities.