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奇拉性对纳米管断裂菌株的影响:全面的机器学习潜力计算
Sherif Abdulkader Tawfik1, Tri Minh Nguyen1, Truyen Tran1
1Applied Artificial Intelligence Institute, Deakin University, Geelong, Victoria 3216, Australia. s.abbas@deakin.edu.au.
这项研究揭示了碳纳米管 (CNT) 性如何使用机器学习模型影响骨折菌株. 扶手椅CNT显示局部断裂,而齐格扎格的CNT显示分布式债券失败.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学计算化学
- 纳米技术 纳米技术
背景情况:
- 碳纳米管 (CNT) 具有独特的机械性能.
- 了解CNT骨折行为对于它们的应用至关重要.
- 奇拉性显著影响CNT的特性.
研究的目的:
- 为了研究CNT性和骨折应变之间的关系.
- 为了利用机器学习潜力模拟CNT断裂.
- 为了识别具有极端骨折菌株的特定CNT配置.
主要方法:
- 使用准静态拉力模拟了186个CNT (4765616个原子) 的断裂.
- 采用了交换潜力的直接集成 (DIEP) 机器学习潜力模型.
- 与已发表的经验模型对比验证的模型预测.
主要成果:
- 识别了扶手椅,齐克扎克和合性CNT的骨折菌株的明显趋势.
- 在扶手椅CNT中观察到局部键裂变,而在曲折式CNT中则观察到分布式断裂.
- 突出显示特定的CNT配置,具有高/低断裂菌株,链接度,直径和缺陷.
结论:
- DIEP的机器学习潜力准确地预测了CNT骨折菌株.
- 性在CNT骨折机制中发挥着关键作用.
- 这种模型对纳米材料的大规模骨折模拟具有前景.
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