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This is a In Vitro Study study.

August 17, 20250 citations

Developing Scalable Reactive Machine Learning Potentials for CHON Chemical Systems

General reactive machine learning potentials for CHON elements

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This research reports a framework enhancing predictive accuracy in chemical reactions of C, H, O, and N, suggesting improved simulation efficiency.

Developing a robust reactive machine learning potential improves predictive accuracy in chemical reaction modeling, enhancing application in catalysis and materials design.
State-of-the-art model accuracy was achieved, closely matching coupled cluster calculations while outperforming conventional density functional theory methods across reactive environments.
The methodology employed a large-scale dataset creation, integrating active learning and semi-empirical labeling for optimal training of machine learning models.
This framework opens pathways for high-fidelity simulations in diverse chemical processes, supporting advancements across multiple scientific and engineering fields.

Li et al. (2025) studied this question.

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August 17, 20250 citations

General reactive machine learning potentials for CHON elements

View Full Paper

Ask AI

Bookmark

Authors

This research reports a framework enhancing predictive accuracy in chemical reactions of C, H, O, and N, suggesting improved simulation efficiency.

Developing a robust reactive machine learning potential improves predictive accuracy in chemical reaction modeling, enhancing application in catalysis and materials design.
State-of-the-art model accuracy was achieved, closely matching coupled cluster calculations while outperforming conventional density functional theory methods across reactive environments.
The methodology employed a large-scale dataset creation, integrating active learning and semi-empirical labeling for optimal training of machine learning models.
This framework opens pathways for high-fidelity simulations in diverse chemical processes, supporting advancements across multiple scientific and engineering fields.

Li et al. (2025) studied this question.

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