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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.

The Quantum-Mechanical Model of an Atom

The Quantum-Mechanical Model of an Atom

Shortly after de Broglie published his ideas that the electron in a hydrogen atom could be better thought of as being a circular standing wave instead of a particle moving in quantized circular orbits, Erwin Schrödinger extended de Broglie’s work by deriving what is now known as the Schrödinger equation. When Schrödinger applied his equation to hydrogen-like atoms, he was able to reproduce Bohr’s expression for the energy and, thus, the Rydberg formula governing...

Newman Projections

Newman Projections

Different notations are used to represent the three-dimensional structure of molecules on two-dimensional surfaces. One of the most commonly used representations is the dash-wedge formula. The dashed wedges, solid wedges, and the plane lines indicate the groups situated behind the plane, coming out of the plane, and in the plane, respectively.
The organic molecules rotate across the single bonds leading to numerous temporary three-dimensional structures of varying energy known as...

VSEPR Theory

VSEPR Theory

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...

Crystal Field Theory - Tetrahedral and Square Planar Complexes

Crystal Field Theory - Tetrahedral and Square Planar Complexes

Tetrahedral Complexes
Crystal field theory (CFT) is applicable to molecules in geometries other than octahedral. In octahedral complexes, the lobes of the dx2−y2 and dz2 orbitals point directly at the ligands. For tetrahedral complexes, the d orbitals remain in place, but with only four ligands located between the axes. None of the orbitals points directly at the tetrahedral ligands. However, the dx2−y2 and dz2 orbitals (along the Cartesian axes) overlap with the ligands less than...

VSEPR Theory and the Basic Shapes

VSEPR Theory and the Basic Shapes

Overview of VSEPR Theory

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Related Experiment Video

Updated: May 9, 2025

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

A Perspective on Foundation Models in Chemistry.

Junyoung Choi¹, Gunwook Nam¹, Jaesik Choi²

¹Department of Chemical and Biological Engineering, and Institute of Chemical Processes, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul 08826, Republic of Korea.

|May 2, 2025

Summary

This summary is machine-generated.

Foundation models, large AI models trained on extensive data, are revolutionizing chemistry research. These models offer solutions for challenges like data scarcity and improving generalization in chemical applications.

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Last Updated: May 9, 2025

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

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Published on: April 12, 2019

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Area of Science:

Artificial Intelligence
Chemistry
Machine Learning

Background:

Foundation models are large-scale, pretrained AI models adaptable to diverse tasks.
Examples like ChatGPT demonstrate their transformative potential in various workflows.
Their success inspires development for complex chemical challenges.

Purpose of the Study:

To review recent advancements in foundation models for chemistry.
To explore their applications across different chemical domains.
To discuss emerging trends and future directions.

Main Methods:

Review of existing literature on foundation models in chemistry.
Analysis of applications in areas like materials discovery and structure-property relationships.
Discussion of how foundation models address limitations of conventional machine learning.

Main Results:

Foundation models are increasingly applied to chemical problems.
They show promise in overcoming data scarcity and generalization issues in machine learning for chemistry.
Progress spans various application scopes within chemistry.

Conclusions:

Foundation models represent a significant paradigm shift in AI for chemistry.
They offer powerful tools for tackling persistent challenges in the field.
Continued research and development are expected to further advance their capabilities and applications.