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 language | English |
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Article number | 1900210 |
Journal | Advanced Theory and Simulations |
Volume | 3 |
Issue number | 2 |
DOIs | |
State | Published - 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).
Funders | Funder number |
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National Science Foundation | ACI-1053575 |
U.S. Department of Energy |
Keywords
- Buckingham potential
- aluminum
- finite-temperature dynamics
- interatomic potential development
- machine learning