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Thermal Sigmatropic Reactions: Overview

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Sigmatropic rearrangements are a class of pericyclic reactions in which a σ bond migrates from one part of a π system to another. These are intramolecular rearrangements where the total number of σ and π bonds remain unchanged.
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Updated: Nov 2, 2025

Synthesis of Zeolites Using the ADOR Assembly-Disassembly-Organization-Reassembly Route

Synthesis of Zeolites Using the ADOR Assembly-Disassembly-Organization-Reassembly Route

Published on: April 3, 2016

Thermodynamic rules for zeolite formation from machine learning based global optimization.

Sicong Ma¹, Cheng Shang¹, Chuan-Ming Wang²

¹Collaborative Innovation Center of Chemistry for Energy Material, Shanghai Key Laboratory of Molecular Catalysis and Innovative Materials, Key Laboratory of Computational Physical Science, Department of Chemistry, Fudan University Shanghai 200433 China zpliu@fudan.edu.cn.

Chemical Science

|June 7, 2021

Summary

This summary is machine-generated.

Machine learning identified thermodynamic rules for stable zeolite frameworks. This advances the rational design and synthesis of novel zeolite structures beyond current limitations.

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Organic Structure-directing Agent-free Synthesis for *BEA-type Zeolite Membrane

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Last Updated: Nov 2, 2025

Synthesis of Zeolites Using the ADOR Assembly-Disassembly-Organization-Reassembly Route

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Published on: April 3, 2016

Adsorption Device Based on a Langatate Crystal Microbalance for High Temperature High Pressure Gas Adsorption in Zeolite H-ZSM-5

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Published on: August 25, 2016

Organic Structure-directing Agent-free Synthesis for *BEA-type Zeolite Membrane

Organic Structure-directing Agent-free Synthesis for *BEA-type Zeolite Membrane

Published on: February 22, 2020

Area of Science:

Materials Science
Computational Chemistry
Chemical Engineering

Background:

Zeolite framework discovery is limited despite theoretical possibilities.
Complex synthesis kinetics and conditions hinder the creation of new zeolite types.

Purpose of the Study:

To establish the global potential energy surface for a representative zeolite system (SiAlP O2H with 12 T atoms).
To identify thermodynamic rules governing the stability of zeolite frameworks.
To correlate these rules with synthesis conditions for rational design.

Main Methods:

Utilized machine learning global optimization techniques.
Analyzed over 10^6 minima data points from atomistic simulations.
Established the global potential energy surface for SiAlP O2H systems.

Main Results:

Identified key thermodynamic rules for stable zeolite energetics.
Discovered local bonding patterns crucial for zeolite stability.
Developed guidelines to classify zeolite types based on stability and composition.

Conclusions:

Machine learning simulations provide a new pathway for zeolite discovery.
Thermodynamic rules enable rational design of stable zeolite frameworks.
This approach facilitates the synthesis of zeolites with desired compositions and properties.