CAREER: Computational Design and Optimization of Operationally Robust Crystal Nucleating Materials via Surface Nano-Patterning

单晶材料在电子、太阳能转换和航空航天工业中的各种应用中的需求很高。单晶的大规模生长通常需要在成核速率足够慢的条件下进行结晶，以防止形成多晶颗粒。这一过程既耗时又昂贵。这项拟议的研究旨在使用先进的计算技术来研究使用被称为晶体成核剂(CNA)的工程化表面来加速单晶的成核和生长。这项研究的目的是发展可用于优化各种材料的晶体生长的基础理论。该项目的主要目标是对CNA表面的纳米级图案化对晶体成核的影响有一个基本的了解，并确定在成核限制范围内的广泛的操作条件下导致成核速率对工艺变量变化的敏感性降低的操作条件。从理论的角度来探索和理解这种以前不为人知的偏离非均相成核的经典图景是有趣的。这项拟议的研究将通过计算探索纳米图案CNA表面的不同特征如何影响成核速率对温度和浓度的敏感度，并利用这一知识来设计在特定成核速率下对操作条件提供最小速率敏感度的最佳CNA表面。从这样的理论和计算研究中获得的见解可以产生设计规则，这些规则可以指导实验工作来设计具有操作稳定性的CNA材料。研究和教育的整合将侧重于为研究生和本科生开发一门新的计算材料科学课程。外联工作将包括针对代表性不足的少数族裔学生的公开介绍和方案编制讲习班，以激励他们在STEM领域寻求职业道路。拟议的项目还包括开发一个高级采样软件包，该软件包将与研究各种物理、化学和生物系统中罕见事件的研究人员公开共享。该奖项反映了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。

Single crystalline materials are in high demand for a variety of applications in the electronics, solar energy conversion and aerospace industries. Large-scale growth of single crystals typically requires conducting crystallization under conditions at which the nucleation rate is slow enough to prevent the formation of polycrystalline particles. This process is time consuming and expensive. The proposed research is aimed at using advanced computational techniques to study the use of engineered surfaces, called crystal nucleating agents (CNAs), for accelerating the nucleation and growth of single crystals. The proposed studies aim to develop fundamental theories that can be used for optimizing crystal growth of a variety of materials.The main objective of this project is to develop a fundamental understanding of the effects of nanoscale patterning of a CNA surface on crystal nucleation and to identify operating conditions leading to reduced sensitivity of the nucleation rate to changes in process variables over a wide range of operating conditions within the nucleation-limited regime. This previously unknown deviation from the classical picture of heterogeneous nucleation is interesting to explore and understand from a theoretical perspective. The proposed research will computationally explore how different features of a nano-patterned CNA surface affect the sensitivity of nucleation rate to temperature and concentration in the nucleation-limited regime, and use that knowledge to design optimal CNA surfaces that offer the smallest sensitivity of rate to operating conditions at specific nucleation rates. The insights obtained from such theoretical and computational investigations can result in design rules that can guide experimental efforts to design operationally robust CNA materials. Integration of research and education will be focused towards developing a new course on Computational Materials Science for graduate and undergraduate students. Outreach efforts will involve public presentations and programming workshops targeting underrepresented minority students to motivate them to pursue career paths in STEM fields. The proposed project also involves the development of an advanced sampling software package that will be publicly shared with researchers that study rare events in a wide variety of physical, chemical and biological systems.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.