Collaborative Research: DMREF: Data-Driven Prediction of Hybrid Organic-Inorganic Structures

合作研究：DMREF：混合有机-无机结构的数据驱动预测

• 批准号: 2323547
• 负责人: David Mitzi
• 金额: $ 50万
• 依托单位: Duke University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2027-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2323547&HistoricalAwards=false
• 关键词: Collaborative; Research; DMREF; Data; Driven

Non-technical Description: Hybrid Organic Inorganic Structures (HOIS), specifically in the form of metal-halide perovskites, have recently attracted much attention due to unprecedented performance advancements in solar cells, light emitting diodes, as well as emerging applications in transistors, sensors, spintronics and catalysts. The extremely wide chemical and structural space engendered by hybrid organic-inorganic systems presents both exciting opportunities for property tunability, but also substantial challenges associated with the laborious process of exploring this wide space for suitable structures for a given application. This project aims to strongly accelerate structure prediction within the HOIS space through exploitation of recently curated X-ray structure databases, molecular dynamics simulation, machine learning (ML), synthetic and structural studies in an iterative feedback loop. The research will provide critical insights into composition-structure relationships, including the preferred structural dimensionality, distortions in the inorganic lattice, relative stabilities of different perovskite-like structures, and the underlying molecular features. The outcome will be the rapid prediction of hybrid organic-inorganic perovskite-type structures from the starting materials, which is essential to optimize optical, electronic and spin properties for a wide range of applications. Approximately one thousand new HOIS will be explored, more than doubling the range of known structures. External collaborations with federal partners at the Air Force Research Laboratory and at the National Renewable Energy Laboratory will test applications of newly synthesized structures and theoretical models. The team includes four Principal Investigators at three universities, including New Mexico Highlands University, a Hispanic-serving institution. The project will train undergraduate, graduate, and PhD-level researchers, including under-represented minorities and females. The PIs also plan to organize symposia at national meetings to disseminate the results and engage further experts in this activity. Technical Description: This research will utilize approximately 1000 reported crystal structures in multiple HOIS databases and molecular dynamics simulations with the INTERFACE force field to inform descriptors and train ML algorithms to predict the relative stability and dimensionality of crystal structures, structural features such as distortions between adjoining octahedra, and lattice parameters. The tools will then be applied to predict the structure of ~1000 yet unknown perovskite compositions in an iterative feedback loop with synthesis and characterization, expecting at least 10 times acceleration relative to serial experimental discovery. Iterations in synthesis, characterization, modeling, and database development will significantly increase the number of known HOIS and elucidate the role of critical intermolecular interactions such as multipolar charge distributions, atomic radii, π-stacking, unusual hydrogen bonds, and chirality of building blocks for the crystal structure and relative stability of HOIS polymorphs. The activity will address a grand challenge in materials science, which consists in obtaining weighted descriptors for precise structural control of HOIS and relationships to crystal growth. The effort will bring together experts and co-advised students across the fields of materials science, chemistry, computation, and data science for accelerated creation of knowledge by Harnessing the Data Revolution and convergent multidisciplinary research. The descriptors, ML algorithms, and training data for structure prediction will be openly shared, taking multiple structure databases, cyberinfrastructure tools, and computing resources to the next level. New database entries, ML algorithms, iteratively improved force field parameters, and experimental techniques can be used for HOIS beyond the scope of this project.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.

非技术描述：杂交有机无机结构（HOIS），特别是以金属 - 六边形钙钛矿的形式，由于在太阳能电池中的前所未有的性能进步，发光二极管的前所未有的性能进步，引起了很多关注，发光二极管以及在晶体管，传感器，传感器，纺纱和猫科动物中的新兴应用。混合有机无机系统所涉及的极宽的化学和结构空间既带来了令人兴奋的财产可可透索的机会，又提供了与实验室探索这个宽敞空间的实验室过程相关的重大挑战。该项目旨在通过开发最近精心策划的X射线结构数据库，分子动力学模拟，机器学习（ML），迭代反馈循环中的合成和结构研究来强烈加速HOI空间内的结构预测。这项研究将提供对组成结构关系的关键见解，包括首选的结构维度，无机晶格中的扭曲，不同的钙钛矿样结构的相对稳定性以及基础分子特征。结果将是从起始材料中快速预测杂交有机胶质钙钛矿型结构，这对于在广泛应用中优化光学，电子和自旋特性至关重要。将探索大约一千个新的HOI，使已知结构的范围增加一倍以上。与空军研究实验室和国家可再生能源实验室的联邦合作伙伴的外部合作将测试新合成的结构和理论模型的应用。该团队包括三所大学的四名主要调查员，包括西班牙裔美国人服务机构的新墨西哥州高地大学。该项目将培训本科，毕业生和博士学位研究人员，包括代表性不足的少数民族和女性。 PI还计划在国家会议上组织研讨会，以传播结果并参与此活动的更多专家。技术描述：本研究将利用大约1000种HOIS数据库中报告的晶体结构，以及具有接口力场的分子动力学模拟，以告知描述符和训练ML ML算法，以预测晶体结构的相对稳定性和尺寸，结构性特征，例如旁边的contahedra和lattice parameters和lattice parameters的扭曲。然后，这些工具将用于预测具有合成和表征的迭代反馈回路中〜1000但未知的钙钛矿组成的结构，相对于串行实验发现，相对于串行实验发现，预期至少10倍加速。综合，表征，建模和数据库开发中的迭代将显着增加已知HOI的数量，并阐明关键分子间相互作用的作用，例如多极电荷分布，原子radii，π堆积，不寻常的氢键，以及构建块的构建块的晶体结构和相对稳定性HOIS POLYMORMORPLEMER PLOLYMORPHS的构件。该活动将解决材料科学的巨大挑战，该挑战包括获得加权描述符，以精确地控制HOIS和与晶体生长的关系。这项工作将通过利用数据革命和收敛的多学科研究来加速知识的创造，从而将专家和共同介绍的学生汇集到材料科学，化学，计算和数据科学领域的专家。将公开共享描述符，ML算法和培训数据以进行结构预测，将多个结构数据库，网络基础结构工具以及将资源计算到一个新的水平。新的数据库条目，ML算法，迭代改进的力场参数和实验技术可用于HOI，超出该项目的范围。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛影响的审查标准来通过评估而被视为珍贵的支持。