AI-enhanced quantum simulations of oxocarbons using random sampling statistics under mechanical compression

This study employs a Δ-machine learning model to derive structure search parameters utilized in the AIRSS package for predicting stable high-density oxocarbon materials. These parameters, including minimum intermolecular distances and densities, guide the search from molecular precursors to transfor...

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Main Authors: Hu, Hang, (James) Ooi, Hsu Kiang, Hu, Anguang
Format: Conference Proceeding
Language:English
Subjects:
Carbon oxides
Chemical bonds
High density
Machine learning
Molecular structure
Parameters
Random sampling
Thermodynamic properties
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(James) Ooi, Hsu Kiang
Hu, Anguang
description This study employs a Δ-machine learning model to derive structure search parameters utilized in the AIRSS package for predicting stable high-density oxocarbon materials. These parameters, including minimum intermolecular distances and densities, guide the search from molecular precursors to transform into solid oxocarbon systems. These oxygenated carbon network solids exhibit stable triangular planar carbon networks. Furthermore, we examine the transformational bonding pathways of C3O2, C5O2, and C7O2 oxocarbon solids under mechanical compression, identifying three stages: van der Waals compression, bond-breaking and forming, and final relaxation. Our findings demonstrate the potential of stable high-density oxocarbon systems across diverse structures. Future research focusing on electronic and thermal properties will be pivotal in realizing their full potential and facilitating widespread adoption.
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source American Institute of Physics:Jisc Collections:Transitional Journals Agreement 2021-23 (Reading list)
subjects Carbon oxides
Chemical bonds
High density
Machine learning
Molecular structure
Parameters
Random sampling
Thermodynamic properties
title AI-enhanced quantum simulations of oxocarbons using random sampling statistics under mechanical compression
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