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Thermodynamic Potentials

Thermodynamic Potentials

Thermodynamic potentials are state functions that are extremely useful in analyzing a thermodynamic system. They have dimensions of energy. The four important thermodynamic potentials are internal energy, enthalpy, Helmholtz free energy, and Gibbs free energy. These thermodynamic potentials can be expressed using two of the following variables: pressure, volume, temperature, and entropy. These two variables are expressed as the rate of change of the thermodynamic potential with respect to other...

Molecular Orbital Theory II

Molecular Orbital Theory II

Molecular Orbital Energy Diagrams

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.

Electronic Structure of Atoms

Electronic Structure of Atoms

An atom comprises protons and neutrons, which are contained inside the dense, central core called the nucleus, with electrons present around the nucleus. Taking into account the wave–particle duality of electrons and the uncertainty in position around the nucleus, quantum mechanics provides a more accurate model for the atomic structure. It describes atomic orbitals as the regions around the nucleus where electrons of discrete energy exist, characterized by four quantum...

Predicting Molecular Geometry

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Lewis Structures of Molecular Compounds and Polyatomic Ions

To draw Lewis structures for complicated molecules and molecular ions, it is helpful to follow a step-by-step procedure as outlined:

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Related Experiment Video

Updated: Jul 6, 2025

Probe Type II Band Alignment in One-Dimensional Van Der Waals Heterostructures Using First-Principles Calculations

Probe Type II Band Alignment in One-Dimensional Van Der Waals Heterostructures Using First-Principles Calculations

Published on: October 12, 2019

A universal graph deep learning interatomic potential for the periodic table.

Chi Chen¹, Shyue Ping Ong²

¹Department of NanoEngineering, University of California, San Diego, CA, USA. chenc273@outlook.com.

Nature Computational Science

|January 4, 2024

Summary

This summary is machine-generated.

A new universal interatomic potential (IAP) called M3GNet, utilizing graph neural networks, accurately predicts material properties. This machine learning model accelerates the discovery of novel, stable, and synthesizable materials.

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Last Updated: Jul 6, 2025

Probe Type II Band Alignment in One-Dimensional Van Der Waals Heterostructures Using First-Principles Calculations

Probe Type II Band Alignment in One-Dimensional Van Der Waals Heterostructures Using First-Principles Calculations

Published on: October 12, 2019

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

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Published on: April 8, 2020

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

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:

Materials Science
Computational Chemistry
Machine Learning

Background:

Interatomic potentials (IAPs) are crucial for atomistic simulations but existing models lack general applicability.
Current IAPs are often limited to specific chemistries or lack the required accuracy for broad use.

Purpose of the Study:

To develop a universal interatomic potential for materials science applications.
To create a machine learning-based IAP capable of handling diverse chemical spaces and predicting material properties accurately.

Main Methods:

Developed M3GNet, a graph neural network-based interatomic potential incorporating three-body interactions.
Trained M3GNet on a large dataset of structural relaxations from the Materials Project.
Applied M3GNet for screening hypothetical crystal structures and predicting material stability.

Main Results:

M3GNet demonstrated broad applicability in structural relaxation, dynamic simulations, and property prediction.
Screening of 31 million hypothetical structures identified 1.8 million potentially stable materials using M3GNet energies.
Density functional theory (DFT) calculations verified the stability of 1,578 out of the top 2,000 lowest-energy materials.

Conclusions:

M3GNet provides a universal and accurate interatomic potential for diverse materials.
Machine learning accelerates the discovery of new, stable, and potentially synthesizable materials.
This approach offers a pathway to discovering materials with exceptional properties.