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A Microfluidic Approach for the Study of Ice and Clathrate Hydrate Crystallization

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Liquid to crystal Si growth simulation using machine learning force field.

Ling Miao¹, Lin-Wang Wang²

¹School of Optical and Electronic Information, Huazhong University of Science and Technology, Wuhan 430074, People's Republic of China.

The Journal of Chemical Physics

|August 24, 2020

Summary

This summary is machine-generated.

Machine learning force fields (ML-FF) accurately simulate far-from-equilibrium silicon crystal growth. This study introduces a bias correction for ML-FF training, crucial for accurate transition temperature predictions.

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Area of Science:

Computational Materials Science
Materials Physics
Machine Learning Applications

Background:

Machine learning force fields (ML-FF) offer ab initio accuracy for large-scale material simulations.
Current ML-FF applications are primarily limited to systems near equilibrium ground states.
Simulating far-from-equilibrium phenomena remains a significant challenge in materials science.

Purpose of the Study:

To investigate the application of ML-FF for simulating far-from-equilibrium liquid to crystal silicon (Si) growth.
To assess the accuracy of ML-FF in reproducing ab initio simulation results for Si growth dynamics.
To compare ML-FF simulations with classical force fields and address potential training biases.

Main Methods:

Development and application of an ML-FF based on ab initio decomposed atomic energy.
Simulation of liquid to crystal Si growth under far-from-equilibrium conditions.
Comparison of ML-FF results with Stillinger-Weber classical force field simulations.
Implementation of a procedure to correct systematic fitting bias in ML-FF training.

Main Results:

The developed ML-FF successfully reproduced all aspects of ab initio simulated Si growth.
Accurate prediction of local energy fluctuations, transition temperatures, diffusion constants, and growth rates.
Significant discrepancies observed between ML-FF and Stillinger-Weber classical force field simulations.
The bias correction procedure was shown to be critical for accurate transition temperature determination.

Conclusions:

ML-FFs are highly effective for simulating complex, far-from-equilibrium material growth processes like Si crystallization.
The proposed bias correction method enhances the reliability and accuracy of ML-FFs for critical thermodynamic properties.
ML-FFs provide a superior alternative to classical force fields for detailed investigations of non-equilibrium material phenomena.