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On-the-Fly Active Learning of Interatomic Potentials for Large-Scale Atomistic Simulations.

Ryosuke Jinnouchi¹, Kazutoshi Miwa¹, Ferenc Karsai²

¹Toyota Central R&D Laboratories., Inc., Aichi 480-1192, Japan.

The Journal of Physical Chemistry Letters

|August 14, 2020

Summary

Active learning generates machine-learning force fields on-the-fly for atomistic simulations. This approach accelerates simulations significantly while maintaining high accuracy by intelligently selecting data for training.

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Area of Science:

Computational Chemistry
Materials Science
Machine Learning

Background:

Atomistic simulations are crucial for understanding materials at the atomic level.
Traditional methods rely heavily on computationally expensive first-principles calculations.
Accelerating these simulations while maintaining accuracy is a key challenge.

Purpose of the Study:

To introduce and explain active-learning schemes for on-the-fly generation of machine-learning force fields.
To highlight the efficiency and accuracy of these self-learning algorithms.
To showcase recent applications demonstrating the power of this approach.

Main Methods:

Utilizing active-learning algorithms to iteratively train machine-learned interatomic potentials.
Implementing query strategies to identify necessary updates to the training dataset.
Performing first-principles calculations only for selected, informative structures.

Main Results:

Machine-learned models are constructed and refined during simulations.
Most first-principles calculations are bypassed, drastically reducing computational cost.
Simulations are accelerated by several orders of magnitude.
High accuracy, comparable to first-principles methods, is retained.

Conclusions:

Active learning offers a powerful paradigm for efficient atomistic simulations.
On-the-fly force field generation significantly speeds up research.
This approach enables large-scale simulations with near first-principles accuracy.