Collaborative Research: Data Driven Discovery of Singlet Fission Materials

合作研究：数据驱动的单线态裂变材料的发现

• 批准号: 2021803
• 负责人: Noa Marom
• 金额: $ 40万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-02-15 至 2024-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2021803&HistoricalAwards=false
• 关键词: Collaborative; Research; Data; Driven; Discovery

NONTECHNICAL SUMMARYThe Division of Materials Research and the Division of Mathematical Sciences contribute funds to this award. This project leverages advances in quantum-mechanics-based materials simulations, data science, and artificial intelligence to discover new solar cell materials. A fraction of excess energy of light in the form of high-energy photons absorbed by solar cell materials can be converted to heat rather than to electric current. This results in a loss of energy conversion efficiency. Many crystals are made of atoms that self-arrange in a regular spatially periodic pattern. Crystals can be made where organic molecules can form the fundamental unit instead of atoms. These molecular crystals may undergo a process known as singlet fission, which enables the conversion of that excess light energy into current carrying charges rather than heat. Singlet-fission-based solar cells are not yet a commercial technology due to dearth of suitable materials that exhibit this process. Over a million molecular crystal structures are known, but it is unknown for which ones singlet fission can occur. Experiments and even advanced quantum mechanical simulations are too costly and time consuming to test many known or predicted new molecular crystals. To overcome this barrier, the interdisciplinary team of experts in computational materials science and statistics will harness methods of data science and artificial intelligence. The team will investigate the use of machine learning methods to accelerate the process of materials discovery by rapidly identifying potentially promising candidate molecular crystals. The team will generate data from quantum-mechanics-based simulations to construct and iteratively improve machine-learned models. This research is expected to lead to the discovery of new materials that exhibit singlet fission, which would advance solar cell technology and could reduce the cost of solar cells. The research will lead to methodological developments that would enable materials discovery for other applications. This project also supports training in advanced high-performance computing at the exascale, and community building at the intersection of materials science and data science through organizing workshops and conferences. An outreach activity will engage educators and K-12 students to raise awareness of careers and research opportunities in materials engineering and data science. TECHNICAL SUMMARYThe Division of Materials Research and the Division of Mathematical Sciences contribute funds to this award. singlet fission is the conversion of one photogenerated singlet exciton into two triplet excitons. There has been much interest in singlet fission because of its potential to significantly increase the efficiency of organic solar cells by harvesting two charge carriers from one photon. However, few materials are presently known to exhibit intermolecular singlet fission with high efficiency, hindering the realization of solid-state singlet-fission-based solar cells. The chemical compound space of possible chromophores is infinitely vast and largely unexplored. To enable computational discovery of materials that exhibit singlet fission, the interdisciplinary team will develop a multi-fidelity screening approach, integrating quantum-mechanics-based simulations at different levels of fidelity with machine learning and database mining. Machine learning algorithms will be used to learn from data generated by simulations and to steer simulations for further data acquisition. The machine learning models will dynamically adapt as more data is acquired. This will be implemented in a fully automated iterative workflow, designed to run on exascale high performance computers. This research will advance the discovery of new intermolecular singlet fission chromophores, which will catalyze the realization of "third generation" solid-state singlet-fission-based organic solar cells. Structure-property correlations revealed by machine learning will advance the fundamental understanding of singlet fission by deriving chemical insights and design rules for chromophores and crystal forms with enhanced singlet-fission efficiency. In addition to singlet-fission chromophores, materials may be discovered with desirable properties for other organic electronic device applications. In addition, this project will lead to new methodology needed to tackle the data-science challenges posed by searching for singlet fission materials. This project will lead to statistical developments in optimal sampling strategies for high-dimensional problems involving many data sources and in adaptive models that dynamically evolve as more data is acquired. The developed approach may be extended to other problems, where properties of interest arise from complex phenomena, for which data acquisition is costly or time consuming, and predictive descriptors are unknown. This project also supports training in advanced high-performance computing at the exascale, and community building at the intersection of materials science and data science through organizing workshops and conferences. An outreach activity will engage educators and K-12 students to raise awareness of careers and research opportunities in materials engineering and data science.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.

非技术摘要材料研究部和数学科学部为该奖项提供资金。该项目利用基于量子力学的材料模拟、数据科学和人工智能的进步来发现新的太阳能电池材料。太阳能电池材料吸收的高能光子形式的一部分多余光能可以转化为热量而不是电流。这导致能量转换效率的损失。许多晶体是由以规则的空间周期模式自我排列的原子组成的。可以在有机分子代替原子形成基本单元的情况下制造晶体。这些分子晶体可能会经历一种称为单线态裂变的过程，该过程能够将多余的光能转化为载流电荷而不是热量。由于缺乏表现出这一过程的合适材料，基于单态裂变的太阳能电池尚未成为商业技术。已知超过一百万个分子晶体结构，但尚不清楚哪些分子晶体结构会发生单线态裂变。实验甚至先进的量子力学模拟都过于昂贵且耗时，无法测试许多已知或预测的新分子晶体。为了克服这一障碍，计算材料科学和统计学的跨学科专家团队将利用数据科学和人工智能的方法。该团队将研究使用机器学习方法，通过快速识别潜在有前途的候选分子晶体来加速材料发现的过程。该团队将从基于量子力学的模拟中生成数据，以构建和迭代改进机器学习模型。这项研究预计将导致发现具有单线态裂变的新材料，这将推进太阳能电池技术并降低太阳能电池的成本。 该研究将带来方法论的发展，使材料发现能够用于其他应用。 该项目还通过组织研讨会和会议，支持百亿亿次高级高性能计算的培训，以及材料科学和数据科学交叉领域的社区建设。一项外展活动将吸引教育工作者和 K-12 学生参与，以提高人们对材料工程和数据科学领域的职业和研究机会的认识。技术摘要材料研究部和数学科学部为该奖项提供资金。 单线态裂变是将一个光生单线态激子转换为两个三线态激子。人们对单线态裂变很感兴趣，因为它有可能通过从一个光子收集两个电荷载流子来显着提高有机太阳能电池的效率。然而，目前已知很少有材料能够表现出高效率的分子间单线裂变，这阻碍了固态单线裂变太阳能电池的实现。可能的发色团的化合物空间无限广阔，并且很大程度上尚未被探索。为了实现单线态裂变材料的计算发现，跨学科团队将开发一种多保真度筛选方法，将不同保真度级别的基于量子力学的模拟与机器学习和数据库挖掘相结合。机器学习算法将用于从模拟生成的数据中学习，并引导模拟进行进一步的数据采集。随着获取更多数据，机器学习模型将动态适应。这将在完全自动化的迭代工作流程中实现，设计为在百亿亿级高性能计算机上运行。这项研究将推进新的分子间单线裂变发色团的发现，这将催化“第三代”固态单线裂变有机太阳能电池的实现。机器学习揭示的结构-性质相关性将通过导出化学见解和生色团和晶体形式的设计规则来促进对单线裂变的基本理解，并提高单线裂变效率。除了单线裂变发色团之外，还可能发现具有其他有机电子器件应用所需特性的材料。此外，该项目将带来解决寻找单线态裂变材料带来的数据科学挑战所需的新方法。该项目将导致涉及许多数据源的高维问题的最佳采样策略的统计发展，以及随着更多数据的获取而动态发展的自适应模型的统计发展。所开发的方法可以扩展到其他问题，在这些问题中，感兴趣的属性是由复杂现象产生的，对于这些问题，数据采集成本高昂或耗时，并且预测描述符未知。该项目还通过组织研讨会和会议，支持百亿亿次高级高性能计算的培训，以及材料科学和数据科学交叉领域的社区建设。一项外展活动将吸引教育工作者和 K-12 学生参与，以提高人们对材料工程和数据科学领域的职业和研究机会的认识。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Benchmarking time-dependent density functional theory for singlet excited states of thermally activated delayed fluorescence chromophores

• DOI: 10.1103/physrevresearch.4.033147
• 发表时间: 2022-08
• 期刊: Physical Review Research
• 影响因子: 4.2
• 作者: Xiaopeng Wang;Siyu Gao;Mingwen Zhao;N. Marom

Finding predictive models for singlet fission by machine learning

• DOI: 10.1038/s41524-022-00758-y
• 发表时间: 2022-04
• 期刊: npj Computational Materials
• 影响因子: 9.7
• 作者: Xingyu Liu;Xiaopeng Wang;Siyu Gao;Vincent Chang;Rithwik Tom;Maituo Yu;L. Ghiringhelli;N. Marom

Multiple resonance induced thermally activated delayed fluorescence: Effect of chemical modification

多重共振诱导的热激活延迟荧光：化学修饰的影响

• DOI: 10.1088/2516-1075/acc70e
• 发表时间: 2023
• 期刊: Electronic Structure
• 影响因子: 2.6
• 作者: Wang, Xiaopeng;Gao, Siyu;Wang, Aizhu;Wang, Bo;Marom, Noa
• 通讯作者: Marom, Noa

An energetics assessment of benzo[ a ]tetracene and benzo[ a ]pyrene as triplet–triplet annihilation emitters

苯并[a]并四苯和苯并[a]芘作为三重态-三重态湮没发射体的能量学评估

• DOI: 10.1039/d2me00004k
• 发表时间: 2022
• 期刊: Molecular Systems Design & Engineering
• 影响因子: 3.6
• 作者: Wang, Xiaopeng;Marom, Noa
• 通讯作者: Marom, Noa