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: 2323548
• 负责人: Luis Raul Castaneda Perea
• 金额: $ 34.67万
• 依托单位: New Mexico Highlands 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=2323548&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：杂化有机无机结构（HOIS），特别是以金属卤化物钙钛矿的形式，最近由于太阳能电池、发光二极管以及晶体管、传感器、自旋电子学和催化剂中的新兴应用的前所未有的性能进步而引起了人们的广泛关注。由混合有机-无机体系产生的极其广泛的化学和结构空间既为性质可调谐性提供了令人兴奋的机会，也为探索用于给定应用的合适结构的这种广泛空间的费力过程带来了实质性挑战。该项目旨在通过利用最近策划的X射线结构数据库，分子动力学模拟，机器学习（ML），迭代反馈回路中的合成和结构研究，大力加速HOIS空间内的结构预测。该研究将提供对组成-结构关系的重要见解，包括优选的结构维度，无机晶格的扭曲，不同钙钛矿结构的相对稳定性以及潜在的分子特征。其结果将是从起始材料中快速预测混合有机-无机钙钛矿型结构，这对于优化广泛应用的光学，电子和自旋特性至关重要。将探索大约一千个新的HOIS，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结构和理论模型的应用。该团队包括三所大学的四名首席研究员，其中包括为西班牙裔服务的新墨西哥州高地大学。该项目将培训本科生、研究生和博士水平的研究人员，包括代表性不足的少数民族和女性。参与者还计划在国家会议上组织专题讨论会，以传播成果，并让更多的专家参与这一活动。技术说明：这项研究将利用多个HOIS数据库中的大约1000个报告的晶体结构和具有INTERFACE力场的分子动力学模拟来通知描述符和训练ML算法，以预测晶体结构的相对稳定性和维度，结构特征，如相邻八面体之间的扭曲和晶格参数。然后，这些工具将被应用于预测约1000种未知钙钛矿组合物的结构，这些组合物将在具有合成和表征的迭代反馈回路中进行预测，预计相对于系列实验发现至少加速10倍。合成、表征、建模和数据库开发中的迭代将显著增加已知HOIS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、原子半径、π堆积、不寻常氢键和HOIS多晶型物的晶体结构和相对稳定性的构建块的手性。该活动将解决材料科学中的一个重大挑战，包括获得用于精确控制HOIS结构的加权描述符以及与晶体生长的关系。这项工作将汇集材料科学，化学，计算和数据科学领域的专家和共同建议的学生，通过利用数据革命和融合的多学科研究来加速知识的创造。用于结构预测的描述符、ML算法和训练数据将开放共享，将多个结构数据库、网络基础设施工具和计算资源提升到一个新的水平。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本项目范围之外的HOIS。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。