How amorphous carbon breaks: atomistic models and machine learning

无定形碳如何断裂：原子模型和机器学习

• 批准号: 2729406
• 金额: --
• 依托单位: University of Warwick
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2729406
• 关键词: How; amorphous; carbon; breaks; atomistic

Amorphous carbon (a-C) has many industrial applications, from electrochemical sensors to wear-resistant coatings. Fracture plays a crucial role in the degradation of its performance, with coatings often failing by shear or flexural cracks. This means that as well as being able to predict fracture toughness, it is crucial to understand the response to mixed tensile and shear loads and predict the trajectory of cracks. In this project, we will build on data-driven approaches that use machine learning techniques to produce quantum mechanically accurate models at a fraction of the cost, and use them to produce a complete description of crack growth in a-C.Only atomistic simulations have the capability of being truly predictive, since larger scale models such as X-FEM, phase-field and others invariably include empirical crack growth algorithms. The project will involve collaboration with Prof. Lars Pastewka at the University of Freiburg, with whom the project supervisor has recently shown that atomistic modelling can be used to produce quantitative predictions of the fracture toughness of a-C in good agreement with experiment [1]. This work used standard continuum linear elastic boundary conditions, and thus required large atomistic domains, preventing extension to crack path selection or mixed-mode loading.The project will also employ a novel numerical continuation enhanced flexible boundary condition scheme, NCFlex, that has recently been developed by the supervisor with Dr Maciej Buze (University of Birmingham) [2] who will also be involved in the team. The approach fuses materials modelling techniques with numerical analysis to produce bifurcation diagrams for cracks.Fracture of a-C represents a "sweet spot" where the process zone is nanoscale and hence accessible to direct atomistic simulation, but still of immediate technological importance. It is currently the only isotropic material whose fracture properties can be studied with predictive atomistic methods. For truly predictive models, improved accuracy is also needed in the interatomic potential. In this project, we will build on data-driven approaches such as [3] that use machine learning techniques to produce QM-accurate force fields at a fraction of the cost, and go beyond tensile loading simulations to produce a complete description of crack growth in a-C.

无定形碳（a-C）具有许多工业应用，从电化学传感器到耐磨涂层。断裂在其性能退化中起着至关重要的作用，涂层经常因剪切或弯曲裂纹而失效。这意味着，除了能够预测断裂韧性外，了解对拉伸和剪切载荷的响应并预测裂纹的轨迹也至关重要。在这个项目中，我们将建立在数据驱动的方法，使用机器学习技术，以一小部分的成本产生量子力学精确的模型，并使用它们来产生一个完整的描述裂纹增长在a-C.只有原子模拟有能力是真正的预测，因为更大规模的模型，如X-FEM，相场和其他总是包括经验裂纹增长算法。该项目将涉及与弗赖堡大学的Lars Pastewka教授的合作，该项目主管最近与他一起证明，原子模型可用于定量预测a-C的断裂韧性，与实验吻合良好[1]。这项工作使用了标准的连续体线弹性边界条件，因此需要大的原子域，防止扩展到裂纹路径选择或混合模式加载。该项目还将采用一种新的数值连续增强柔性边界条件方案NCFlex，该方案最近由主管Maciej Buze博士（伯明翰大学）[2]开发，他也将参与该团队。该方法融合了材料建模技术与数值分析，产生分叉图cracks.Fracture的a-C代表一个“甜蜜点”的过程区是纳米级的，因此可以直接原子模拟，但仍然是直接的技术重要性。它是目前唯一的各向同性材料，其断裂性能可以用预测原子方法进行研究。对于真正的预测模型，还需要提高原子间势的准确性。在这个项目中，我们将建立在数据驱动的方法上，例如[3]使用机器学习技术以一小部分成本产生QM精确的力场，并超越拉伸载荷模拟，以产生a-C中裂纹生长的完整描述。