Interatomic Potential Model Development: Finite-Temperature Dynamics Machine Learning

Jiaqi Wang, Seungha Shin, Sangkeun Lee

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Developing an accurate interatomic potential model is a prerequisite for achieving reliable results from classical molecular dynamics (CMD) simulations; however, most of the potentials are biased as specific simulation purposes or conditions are considered in the parameterization. For developing an unbiased potential, a finite-temperature dynamics machine learning (FTD-ML) approach is proposed, and its processes and feasibility are demonstrated using the Buckingham potential model and aluminum (Al) as an example. Compared with conventional machine learning approaches, FTD-ML exhibits three distinguished features: 1) FTD-ML intrinsically incorporates more extensive configurational and conditional space for enhancing the transferability of developed potentials; 2) FTD-ML employs various properties calculated directly from CMD, for ML model training and prediction validation against experimental data instead of first-principles data; 3) FTD-ML is much more computationally cost effective than first-principles simulations, especially when the system size increases over 103 atoms as employed in this research for ensuring reliable training data. The Al Buckingham potential developed by the FTD-ML approach exhibits good performance for general simulation purposes. Thus, the FTD-ML approach is expected to contribute to a fast development of interatomic potential model suitable for various simulation purposes and conditions, without limitation of model type, while maintaining experimental-level accuracy.

Original languageEnglish
Article number1900210
JournalAdvanced Theory and Simulations
Volume3
Issue number2
DOIs
StatePublished - Feb 1 2020

Funding

This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. This work utilized the resources of Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by National Science Foundation Grant number ACI-1053575. The authors sincerely thank for the fruitful discussion with Dr. Dongwon Shin (Oak Ridge National Lab).

FundersFunder number
National Science FoundationACI-1053575
U.S. Department of Energy

    Keywords

    • Buckingham potential
    • aluminum
    • finite-temperature dynamics
    • interatomic potential development
    • machine learning

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