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MLIP-3: Active learning on atomic environments with moment tensor potentials.

Evgeny Podryabinkin¹, Kamil Garifullin², Alexander Shapeev¹

¹Skolkovo Institute of Science and Technology, Skolkovo Innovation Center, Bolshoy boulevard 30, Moscow 143026, Russian Federation.

The Journal of Chemical Physics

|August 28, 2023

Summary

This summary is machine-generated.

Researchers introduce MLIP-3, a new package for creating and training moment tensor potentials. This enhanced software improves atomistic simulations using machine learning potentials and active learning on atomic neighborhoods.

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Area of Science:

Computational materials science
Machine learning in physics

Background:

Sharing research code is crucial in atomistic modeling.
Machine-learning potentials are rapidly advancing the field.
Existing packages facilitate classical and quantum-mechanical modeling.

Purpose of the Study:

Introduce the MLIP-3 package for constructing and training moment tensor potentials.
Enhance atomistic simulations through improved machine learning potentials.
Leverage active learning on atomic neighborhoods for large-scale simulations.

Main Methods:

Development of the MLIP-3 software package.
Implementation of moment tensor potentials.
Application of active learning techniques for model training.

Main Results:

MLIP-3 offers improved capabilities over its predecessor, MLIP-2.
The package facilitates efficient construction and training of moment tensor potentials.
Active learning is applied to atomic neighborhoods for enhanced simulation accuracy.

Conclusions:

MLIP-3 represents a significant advancement in atomistic modeling software.
The package supports the growing field of machine-learning potentials.
Enhanced active learning capabilities improve the efficiency and accuracy of large-scale simulations.