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Probing C84-embedded Si Substrate Using Scanning Probe Microscopy and Molecular Dynamics

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Uncertainty quantification in atomistic simulations of silicon using interatomic potentials.

I R Best¹, T J Sullivan^1,2, J R Kermode¹

¹Warwick Centre for Predictive Modelling, School of Engineering, University of Warwick, Coventry CV4 7AL, United Kingdom.

The Journal of Chemical Physics

|August 14, 2024

Summary

This summary is machine-generated.

Quantifying simulation errors is crucial. This study uses conformal prediction with atomic cluster expansion potentials to provide calibrated error bars for silicon properties, improving simulation reliability.

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Area of Science:

Computational Materials Science
Atomistic Simulations
Predictive Modeling

Background:

Atomistic simulations use interatomic potentials for larger scales than first-principles methods.
Parameterized potentials introduce inaccuracies compared to the true potential energy surface.
Quantifying simulation uncertainty is essential for result confidence and improvement metrics.

Purpose of the Study:

To develop a method for quantifying uncertainty in atomistic simulations.
To provide calibrated error bars for key material properties.
To assess the impact of different potentials and training sets on uncertainty bounds.

Main Methods:

Formation of ensembles of atomic cluster expansion potentials.
Application of conformal prediction with ab initio training data.
Calculation of bulk modulus, elastic constants, vacancy formation energy, and migration barrier for silicon.

Main Results:

Meaningful, calibrated error bars were successfully generated for silicon properties.
The study evaluated the influence of various potentials and training datasets on uncertainty quantification.
Demonstrated a robust approach for error estimation in atomistic simulations.

Conclusions:

Conformal prediction offers a reliable method for error quantification in atomistic simulations.
The developed approach enhances confidence in simulation results and guides potential improvements.
This work provides a framework for uncertainty assessment in materials simulations.