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This research aims to understand the mechanisms of coal pyrolysis at the atomic level to enhance coal utilization and energy strategies.

June 5, 2026ChemistryOpen Access

Molecular Dynamics Study on the Mechanism of Coal High-Temperature Pyrolysis Based on Machine Learning Potential

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Randomized trial examines temperature effects on coal pyrolysis products, suggesting improved energy strategies.

This research aims to understand the mechanisms of coal pyrolysis at the atomic level to enhance coal utilization and energy strategies.
Proposed a multiscale computational framework integrating density functional theory (DFT) and machine learning potential (MLP) methods.
Constructed and benchmarked two coal-specific machine learning potentials: DPA3-coal and DPA3-coal@dftb.
Performed accelerated reactive molecular dynamics simulations on a Solomon-type bituminous coal molecule across temperatures from 1600 to 2600 K.
DPA3-coal@dftb showed improved accuracy in energy and atomic force predictions compared to ReaxFF.
Temperature-dependent evolution of coke, tar, and gas products was observed, indicating complex reaction pathways.
Higher-level DFT calculations verified the consistency of key reaction pathways, confirming the effectiveness of coal-specific MLPs.

Ren et al. (2026) studied this question.

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June 5, 2026ChemistryOpen Access

View Full Paper

Ask AI

Bookmark

Authors

Randomized trial examines temperature effects on coal pyrolysis products, suggesting improved energy strategies.

This research aims to understand the mechanisms of coal pyrolysis at the atomic level to enhance coal utilization and energy strategies.
Proposed a multiscale computational framework integrating density functional theory (DFT) and machine learning potential (MLP) methods.
Constructed and benchmarked two coal-specific machine learning potentials: DPA3-coal and DPA3-coal@dftb.
Performed accelerated reactive molecular dynamics simulations on a Solomon-type bituminous coal molecule across temperatures from 1600 to 2600 K.
DPA3-coal@dftb showed improved accuracy in energy and atomic force predictions compared to ReaxFF.
Temperature-dependent evolution of coke, tar, and gas products was observed, indicating complex reaction pathways.
Higher-level DFT calculations verified the consistency of key reaction pathways, confirming the effectiveness of coal-specific MLPs.

Ren et al. (2026) studied this question.

View Full Paper

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Bookmark