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.

非技术摘要这一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。当前，由于可以在三维空间中以大量方式包装分子，因此在很大程度上缺少用于开发具有靶向物理特性的新有机材料的系统预测模型。这项研究旨在开发一种新的计算方案，该方案可以根据现代晶体学对有机材料进行更有效的发现。为了实现这一目标，PI首先将复杂的晶体学数据数字化为低维表示，以保留有关分子填料和晶体对称性的关键信息。其次，将开发一条计算管道，以通过不同级别的理论对有机材料进行建模。最后，通过具有出色的机械和铁电特性的有机材料进行计算筛选，可以在现代技术中找到潜在的应用，从而验证新基础设施的力量。该职业奖还为促进开放源软件开发的教育和外展活动提供了支持。具体目标包括（i）培训计算物理学的本科生和研究生； （ii）为K-12学生提供科学计算和Python编程中的夏季/冬季营地； （iii）组织研讨会，以了解晶体学和材料建模中的开源代码。技术摘要职业奖旨在开发用于数字晶体学的计算管道，以加快发现各种应用的小分子有机材料。如今，科学家正在寻求通过使用具有天文学大型设计空间的化学和结构操作来改善有机材料的功能。为了开发新的有机固体，水晶工程师诉诸预测指南。然而，由于复杂的分子堆积，与无机对应物相比，系统地发现来自计算机模拟的新有机晶体仍然具有挑战性。在这个项目中，目标是通过对复杂晶体堆积及其对物理特性的影响进行数据密集型研究来开发一种新的方法来设计有机材料。具体而言，PI将基于现代数学理论引入有机晶体数据的数字化描述，并将其实施到开源代码pyxtal中。将开发一条计算管道，以允许从分子力学，机器学习到量子力学的不同水平的高通量材料建模。基于这种基础设施，将对具有优质机械强度和铁电特性的有机材料进行计算筛选。该职业项目的教育组成部分将集中在促进开源软件开发方面。具体目标包括（i）培训计算物理学的本科生和研究生； （ii）为K-12学生提供科学计算和Python编程中的夏季/冬季营地； （iii）组织研讨会，以学习晶体学和材料建模中的开源法规。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的评估评估的评估来支持的。