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Crystal Field Theory - Octahedral Complexes

Crystal Field Theory - Octahedral Complexes

Crystal Field Theory
To explain the observed behavior of transition metal complexes (such as colors), a model involving electrostatic interactions between the electrons from the ligands and the electrons in the unhybridized d orbitals of the central metal atom has been developed. This electrostatic model is crystal field theory (CFT). It helps to understand, interpret, and predict the colors, magnetic behavior, and some structures of coordination compounds of transition metals.
CFT focuses on...

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Updated: Jan 16, 2026

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

PSCG-Net: A Multiscale Crystal Graph Neural Network for Accelerated Materials Discovery.

Guangyao Chen^1,2, Zhilong Wang^1,2, Fengqi You^1,2,3

¹AI for Science Institute (CUAISci), Cornell University, Ithaca, New York 14853, United States.

Journal of Chemical Information and Modeling

|September 29, 2025

Summary

A new pair-scalable crystal graph neural network (PSCG-Net) accurately models long-range atomic interactions in materials. This advances the discovery of novel materials for energy and electronics applications.

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Area of Science:

Materials Science
Computational Chemistry
Artificial Intelligence

Background:

Accurate prediction of material properties is vital for technological advancement.
Traditional graph neural networks (GNNs) struggle with long-range interactions in crystalline materials due to fixed cutoff radii.
This limitation hinders the discovery of new materials for energy, electronics, and sustainable technologies.

Purpose of the Study:

To introduce a novel machine learning framework, the pair-scalable crystal graph neural network (PSCG-Net), designed to overcome the limitations of traditional GNNs.
To enhance the accurate prediction of material properties by incorporating multiscale structural representations.
To accelerate the design and discovery of high-performance materials.

Main Methods:

Development of PSCG-Net, a graph neural network incorporating multiscale structural representations inspired by the pair distribution function.
Utilizing graphs with various cutoff distances to capture both short-range and long-range atomic interactions.
Testing PSCG-Net on over 150,000 crystal structures for formation energy prediction and band gap analysis.

Main Results:

PSCG-Net achieved a mean absolute error of 0.065 eV in formation energy prediction, outperforming the baseline Crystal Graph Convolutional Neural Network.
Consistent performance across six diverse datasets and validation through first-principles calculations for band gap predictions.
Demonstrated practical utility in screening high-performance materials for photovoltaics, dielectrics, and superconductors.

Conclusions:

PSCG-Net accurately captures hierarchical atomic interactions, significantly improving predictive accuracy in materials science.
The framework accelerates the design and discovery of novel materials.
PSCG-Net offers a versatile approach applicable to multiscale challenges across scientific disciplines, paving the way for technological innovation.