Issue 45, 2023

Strategic sampling with stochastic surface walking for machine learning force fields in iron's bcc–hcp phase transitions

Abstract

This study developed a machine learning-based force field for simulating the bcc–hcp phase transitions of iron. By employing traditional molecular dynamics sampling methods and stochastic surface walking sampling methods, combined with Bayesian inference, we construct an efficient machine learning potential for iron. By using SOAP descriptors to map structural data, we find that the machine learning force field exhibits good coverage in the phase transition space. Accuracy evaluation shows that the machine learning force field has small errors compared to DFT calculations in terms of energy, force, and stress evaluations, indicating excellent reproducibility. Additionally, the machine learning force field accurately predicts the stable crystal structure parameters, elastic constants, and bulk modulus of bcc and hcp phases of iron, and demonstrates good performance in predicting higher-order derivatives and phase transition processes, as evidenced by comparisons with DFT calculations and existing experimental data. Therefore, our study provides an effective tool for investigating the phase transitions of iron using machine learning methods, offering new insights and approaches for materials science and solid-state physics research.

Graphical abstract: Strategic sampling with stochastic surface walking for machine learning force fields in iron's bcc–hcp phase transitions

Article information

Article type
Paper
Submitted
12 Jul 2023
Accepted
24 Oct 2023
First published
30 Oct 2023
This article is Open Access
Creative Commons BY-NC license

RSC Adv., 2023,13, 31728-31737

Strategic sampling with stochastic surface walking for machine learning force fields in iron's bcc–hcp phase transitions

F. Wang, Z. Yang, F. Li, J. Shao and L. Xu, RSC Adv., 2023, 13, 31728 DOI: 10.1039/D3RA04676A

This article is licensed under a Creative Commons Attribution-NonCommercial 3.0 Unported Licence. You can use material from this article in other publications, without requesting further permission from the RSC, provided that the correct acknowledgement is given and it is not used for commercial purposes.

To request permission to reproduce material from this article in a commercial publication, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party commercial publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements