CAREER: Organic Materials Discovery with the Aid of Digital Crystallography

职业：借助数字晶体学发现有机材料

• 批准号: 2410178
• 负责人: Qiang Zhu
• 金额: $ 53.16万
• 依托单位: University of North Carolina at Charlotte
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-04-01 至 2027-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2410178&HistoricalAwards=false
• 关键词: CAREER; Organic; Materials; Discovery; Aid

NONTECHNICAL SUMMARYThis CAREER award supports theoretical and computational research and educational activities to advance the understanding of organic materials. Currently, systematic predictive models for developing new organic materials with targeted physical properties are largely missing, due to the fact that molecules can be packed in enormous number of ways in the three dimensional space. This research aims to develop a new computational scheme that allows more efficient discovery of organic materials based on modern crystallography. To achieve this objective, the PI will first digitize the complex crystallographic data into low-dimensional representations that retain the key information about the molecular packing and crystal symmetry. Second, a computational pipeline will be developed to model the organic materials though different levels of theory. Finally, the power of the new infrastructure will be validated by conducting computational screening of organic materials with superior mechanical and ferroelectric properties that can find potential applications in modern technologies.This CAREER award also provides support for education and outreach activities that are centered on promoting open-source software development. Specific objectives include (i) training undergraduate and graduate students in computational physics; (ii) holding the summer/winter camps in scientific computing and Python programming for K-12 students; and (iii) organizing workshops for learning about open-source codes in crystallography and materials modeling.TECHNICAL SUMMARYThis CAREER award aims to develop a computational pipeline for digital crystallography that can speed up the discovery of small molecule organic materials for various applications. Nowadays, scientists are seeking to improve the functionalities of organic materials by using chemical and structural manipulations with a design space that can be astronomically large. To develop new organic solids, crystal engineers resort to predictive guidelines. However, systematically discovering new organic crystals from computer simulations remains challenging due to the complex molecular packing as compared to the inorganic counterpart. In this project, the goal is to develop a new approach to design organic materials by performing a data-intensive investigation of complex crystal packing and its impacts on the physical properties. Specifically, the PI will introduce a digitized description for organic crystal data based on modern mathematical theory and implement them into the open-source code PyXtal. A computational pipeline will be developed to allow high-throughput materials modeling at different levels from molecular mechanics, machine learning to quantum mechanics. Based on this infrastructure, computational screenings of organic materials with superior mechanical strength and ferroelectric properties will be conducted. The educational component of this CAREER project will be centered on promoting open-source software development. Specific objectives include (i) training undergraduate and graduate students in computational physics; (ii) holding the summer/winter camps in scientific computing and Python programming for K-12 students; and (iii) organizing workshops for learning about open-source codes in crystallography and materials modeling.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.

非技术总结这个职业奖支持理论和计算研究和教育活动，以促进对有机材料的理解。目前，用于开发具有目标物理特性的新有机材料的系统预测模型在很大程度上缺失，这是由于分子可以在三维空间中以大量方式包装的事实。这项研究旨在开发一种新的计算方案，允许基于现代晶体学更有效地发现有机材料。为了实现这一目标，PI将首先将复杂的晶体学数据转换为低维表示，保留有关分子堆积和晶体对称性的关键信息。其次，将开发一个计算管道，通过不同层次的理论来模拟有机材料。最后，通过计算机筛选具有上级机械性能和铁电性能的有机材料来验证新基础设施的能力，这些材料可以在现代技术中找到潜在的应用。该CAREER奖还为以促进开源软件开发为中心的教育和推广活动提供支持。具体目标包括：（一）培训计算物理学的本科生和研究生;（二）为K-12学生举办科学计算和Python编程的夏/冬令营;及（iii）举办工作坊，让市民了解开放式教育的概念，晶体学和材料建模的源代码。技术总结这个职业生涯奖旨在开发一个数字晶体学的计算管道，可以加快发现小的分子有机材料的各种应用。如今，科学家们正在寻求通过使用化学和结构操作来改善有机材料的功能，其设计空间可以是天文数字大。为了开发新的有机固体，晶体工程师们求助于预测准则。然而，系统地发现新的有机晶体从计算机模拟仍然具有挑战性，由于复杂的分子包装相比，无机对应物。在这个项目中，目标是通过对复杂晶体堆积及其对物理性质的影响进行数据密集型调查，开发一种设计有机材料的新方法。具体来说，PI将引入基于现代数学理论的有机晶体数据的数字化描述，并将其实现为开源代码PyXtal。将开发一个计算管道，以允许从分子力学，机器学习到量子力学的不同层次的高通量材料建模。在此基础上，将对具有上级机械强度和铁电性能的有机材料进行计算筛选。这个CAREER项目的教育部分将集中在促进开源软件开发。具体目标包括：（一）培训计算物理学的本科生和研究生;（二）为K-12学生举办科学计算和Python编程的夏/冬令营;及（iii）举办工作坊，让市民了解开放式教育的概念，该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识产权进行评估来支持。优点和更广泛的影响审查标准。