Chen Kai, Yang Riyi, Wang Zhefeng, Zhao Wuyan, Xu Youmin, Sun Huaijun, Zhang Chao, Wang Songyou, Ho Kaiming, Wang Cai-Zhuang, Su Wan-Sheng
Shanghai Ultra-Precision Optical Manufacturing Engineering Center, Department of Optical Science and Engineering, Fudan University, Shanghai 200433, China.
Jiyang College of Zhejiang Agriculture and Forestry University, Zhuji 311800, China.
Phys Chem Chem Phys. 2024 Oct 17;26(40):25936-25945. doi: 10.1039/d4cp02781g.
Small-scale systems based on periodic boundary conditions often cannot accurately describe real-world situations, especially when conducting molecular dynamics simulations to study phase transitions, where it is very necessary to use large-scale systems. However, studying phase transitions in large-scale systems is an important and difficult task. Though molecular dynamics (AIMD), based on density functional theory (DFT), provides advantages in terms of accuracy, it is very difficult to study phase transitions in large-scale systems due to the considerable computational time required. In addition, although traditional empirical potentials are faster, their lower calculation accuracy makes it difficult to use them for phase transition studies. It is crucial to devise a method that has enabled a promising fusion of computational efficiency and precision to effectively investigate phase transitions in large-scale systems. In this work, the obtained machine learning potential function of carbon through deep neural networks not only demonstrates strong scalability but also effectively enables the study of the formation mechanisms of amorphous diamond and polycrystalline diamond using C crystals and graphene as precursors under high-pressure high-temperature conditions (HPHT). Furthermore, the structure search software (AIRSS) was used to generate numerous initial structures which were optimized using the machine learning potential, a process which led to finding new structural clusters of carbon. Interestingly, the predictive capabilities of the machine learning potential for symmetric and asymmetric carbon clusters aligned well with the Gaussian approximation potential (GAP), yet the former demonstrated higher computational efficiency, making it more suitable for carbon material research. The results of this work signify significant progress in the field of carbon transition study, opening up new possibilities for exploring and understanding carbon materials with improved computational efficacy.
基于周期性边界条件的小规模系统往往无法准确描述现实世界的情况，尤其是在进行分子动力学模拟以研究相变时，此时使用大规模系统非常必要。然而，研究大规模系统中的相变是一项重要且困难的任务。尽管基于密度泛函理论（DFT）的分子动力学（AIMD）在准确性方面具有优势，但由于所需的计算时间相当长，很难用于研究大规模系统中的相变。此外，尽管传统的经验势计算速度更快，但其较低的计算精度使其难以用于相变研究。设计一种能够将计算效率和精度有效融合的方法对于有效研究大规模系统中的相变至关重要。在这项工作中，通过深度神经网络获得的碳的机器学习势函数不仅具有很强的可扩展性，而且能够有效地研究在高温高压（HPHT）条件下以C晶体和石墨烯为前驱体的非晶金刚石和多晶金刚石的形成机制。此外，使用结构搜索软件（AIRSS）生成了大量初始结构，并使用机器学习势对其进行了优化，这一过程导致发现了新的碳结构簇。有趣的是，机器学习势对对称和不对称碳簇的预测能力与高斯近似势（GAP）吻合得很好，但前者具有更高的计算效率，使其更适合碳材料研究。这项工作的结果标志着碳转变研究领域取得了重大进展，为以更高的计算效率探索和理解碳材料开辟了新的可能性。
