CAREER: Atomistic Investigation of Phase Transition in Nanostructured Silicon--Towards Convergent Understanding with Mechanics-Informed Machine Learning Potential

该学院早期职业发展（CAREER）奖将支持硅纳米结构中相变和塑性的基础研究。纳米结构硅，如纳米颗粒、纳米柱和纳米线，由于其独特的机械、光学和电学性质，已被广泛用于集成电路、微机电系统和纳米机电系统以及光电子器件。这项研究所产生的知识将被应用于提高硅纳米器件的机械可靠性，改善硅材料的加工，并探索硅相工程的新方法，从而促进国民健康，繁荣和福利。此外，机器学习方法将提供一个创新的计算框架，以开发力学激发的原子间势，以高保真度研究原子尺度下硅和其他材料的变形。这项研究将通过两项活动纳入本科和研究生教育。一套名为“材料的原子观”的计算模块将被设计并整合到机械工程课程的力学课程中。这些模块将帮助学生可视化材料的力学行为，并接受高级建模和仿真技能的培训。此外，该项目还将通过实习促进代表性不足的少数民族参与研究。最近的纳米力学实验显示，纳米结构硅中的相变在许多方面与体硅不同，具有许多独特而有趣的特征。然而，在原子尺度上相成核和增长的动力学机制仍然未知。本计画的研究目标是定量地确定应力场、晶粒/相界以及塑性变形对矽奈米结构中相转变的影响。值得注意的是，没有可靠的原子间势来描述硅相变，因此将开发一种基于深度神经网络的新型机械信息机器学习势来填补差距。将使用包含应力依赖性相变最小能量路径的数据集来训练势能，这将提供足够的分辨率来对高应力下的能量景观进行采样。相变将用分子动力学模拟和有限变形轻推弹性带方法相结合来研究。此外，一个新的有限变形二聚体方法将被开发的基础上，传统的二聚体方法探测在高应力和有限变形下的相形核，而不必指定一个最终状态的结构。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This Faculty Early Career Development (CAREER) award will support fundamental research on phase transition and plasticity in silicon nanostructures. Nanostructured silicon, such as nanoparticles, nanopillars, and nanowires have been widely used for integrated circuits, micro and nano-electromechanical systems, and photovoltaics due to the unique mechanical, optical, and electrical properties. The knowledge generated in this research will be applied to enhance the mechanical reliability of silicon nanodevices, to improve the machining of silicon materials, and to explore novel approaches for silicon phase engineering, thereby advancing national health, prosperity, and welfare. In addition, the machine learning approach will provide an innovative computational framework to develop mechanics-inspired interatomic potentials to study the deformation of silicon and other materials at the atomic scale with high fidelity. The research will be integrated into undergraduate and graduate education through two activities. A set of computation modules called “The Atomic View of Materials” will be designed and integrated into mechanics courses in the mechanical engineering curriculum. These modules will help students visualize the mechanical behavior of materials and get trained on advanced modeling and simulation skills. In addition, this project will boost the participation of underrepresented minorities in research through internships.Recent nanomechanical experiments have shown many unique and intriguing features of phase transitions in nanostructured silicon that are different from bulk silicon in many aspects. The kinetic mechanism of phase nucleation and propagation at atomic scale remains unknown, however. The research objective of this project is to quantitatively determine the roles of stress field, grain/phase boundaries, and plastic deformation on phase transition in silicon nanostructures. It is noted that there is no reliable interatomic potential to describe silicon phase transition, thus a novel mechanics-informed machine learning potential based on deep neural network will be developed to fill the gap. The potential will be trained with the dataset containing stress-dependent phase transition minimum energy paths, which will provide sufficient resolution to sample the energy landscape under high stress. The phase transition will be investigated with a combination of molecular dynamics simulations and finite deformation nudged elastic band method. In addition, a new finite deformation Dimer method will be developed based on the conventional Dimer method to probe phase nucleation under high stress and finite deformation without having to specify a final state structure. The results generated in this project will be used to understand experimental observations from literature and collaborators.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.