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奖将支持硅纳米结构相变和可塑性的基础研究。纳米硅由于其独特的机械、光学和电学性质，已被广泛应用于集成电路、微纳机电系统和光伏等领域。这项研究所产生的知识将应用于提高硅纳米器件的机械可靠性，改进硅材料的加工，探索硅相工程的新方法，从而促进国民的健康、繁荣和福祉。此外，机器学习方法将提供一个创新的计算框架，以开发力学启发的原子间势，以高保真地研究硅和其他材料在原子尺度上的变形。这项研究将通过两项活动整合到本科和研究生教育中。我们将设计一套名为《材料原子观》的计算模块，并将其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，并接受高级建模和模拟技能的培训。此外，该项目将通过实习促进未被充分代表的少数群体参与研究。最近的纳米机械实验显示，纳米结构硅中的相变具有许多在许多方面不同于体硅的独特而有趣的特征。然而，原子尺度上相形核和传播的动力学机制尚不清楚。本项目的研究目标是定量确定应力场、晶界和塑性变形在硅纳米结构相变中的作用。由于没有可靠的原子间势来描述硅的相变，因此将开发一种基于深度神经网络的力学信息的机器学习势来填补这一空白。势将用包含应力依赖的相变最小能量路径的数据集来训练，这将提供足够的分辨率来采样高应力下的能量景观。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的方法来研究。此外，还将在传统的二聚体方法的基础上发展一种新的有限变形二聚体方法，用于探测高应力和有限变形下的相核，而不必指定最终的状态结构。这个项目产生的结果将被用来理解文献和合作者的实验观察。这个奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.