Interatomic Potential Model Development: Finite‐Temperature Dynamics Machine Learning. Issue 2 (17th December 2019)

• Record Type: Journal Article
• Authors: Wang, Jiaqi; Shin, Seungha; Lee, Sangkeun
• Is Part Of: Advanced theory and simulations. Volume 3:Issue 2(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-12-17
• Subjects: aluminum -- Buckingham potential -- finite‐temperature dynamics -- interatomic potential development -- machine learning; Science -- Simulation methods -- Periodicals; Science -- Methodology -- Periodicals; Engineering -- Simulation methods -- Periodicals; Engineering -- Methodology -- Periodicals; 507.21
• DOI: 10.1002/adts.201900210
• Languages: English
• ISSNs: 2513-0390

Abstract

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 10 3 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. Abstract : A finite‐temperature dynamics machine learning (FTD‐ML) approach successfully develops a Buckingham potential for aluminum with high accuracy and efficiency, which can well reproduce structural, thermodynamics, and mechanical properties. Compared with a traditional ML approach employing density functional theory data, FTD‐ML is more efficient, accurate, and promising, regardless of material and interatomic potential type.