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Interatomic Potential Model Development: Finite‐Temperature Dynamics Machine Learning
Advanced Theory and Simulations ( IF 3.3 ) Pub Date : 2019-12-17 , DOI: 10.1002/adts.201900210
Jiaqi Wang 1 , Seungha Shin 1 , Sangkeun Lee 2
Affiliation  

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.

中文翻译:

原子间势模型开发:有限温度动力学机器学习

建立精确的原子间电势模型是从经典分子动力学(CMD)模拟获得可靠结果的前提。但是,由于在参数化过程中考虑了特定的仿真目的或条件,因此大多数电位都存在偏差。为了开发无偏势,提出了一种有限温度动力学机器学习(FTD-ML)方法,并以白金汉势模型和铝(Al)为例演示了其过程和可行性。与传统的机器学习方法相比,FTD-ML具有三个显着特征:1)FTD-ML本质上包含了更广泛的配置和条件空间,以增强已开发潜力的可传递性;2)FTD‐ML采用直接从CMD计算的各种属性,针对ML模型的训练和针对实验数据(而非第一性原理数据)的预测验证;3)FTD-ML在计算上比第一原理模拟更具成本效益,特别是当系统大小增加到10以上时本研究中使用3个原子来确保可靠的训练数据。FTD-ML方法开发的Al Buckingham电势在常规模拟中表现出良好的性能。因此,FTD-ML方法有望促进原子间电势模型的快速开发,该模型适用于各种模拟目的和条件,而不受模型类型的限制,同时保持实验水平的准确性。
更新日期:2020-03-04
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