EAGER: MATDAT18 Type-1: Collaborative Research: Data Driven Discovery of Singlet Fission Materials

EAGER：MATDAT18 Type-1：协作研究：数据驱动的单线态裂变材料发现

• 批准号: 1844492
• 负责人: Brian Reich
• 金额: $ 6.22万
• 依托单位: North Carolina State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1844492&HistoricalAwards=false
• 关键词: EAGER; MATDAT18; Type; Collaborative; Research

NONTECHNICAL SUMMARYThis award supports continued collaboration of materials researchers with data scientists kindled at the MATDAT18 Datathon event. The efficiency of organic solar cells may be enhanced significantly by harnessing singlet fission (SF), a quantum mechanical process that can lead to the generation of two conducting species, for example electrons, from one quantum of light. Presently, few materials are known to exhibit intermolecular SF in the solid state, and they belong to restricted chemical families. The vast number of possible molecules and crystals that could be made has not been explored for SF. The PIs will use computer simulations to search the many possibilities for new SF materials. To this end, a new approach will be developed, one that integrates cutting-edge advances in quantum mechanical simulations and machine learning. This research will advance both fields of materials science and data science. Graduate and undergraduate students will train in a collaborative cross-disciplinary environment at the interface of computational materials science and data science and acquire transferrable job skills in high demand. TECHNICAL SUMMARYThis award supports continued collaboration of materials researchers with data scientists kindled at the MATDAT18 Datathon event. Singlet fission (SF) is the conversion of one photogenerated singlet exciton into two triplet excitons. Recently, there has been a surge of interest in SF thanks to 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 SF with high efficiency, hindering the realization of solid-state SF-based solar cells. The chemical compound space of possible chromophores is infinitely vast and largely unexplored. To enable computational discovery of SF materials, a new multi-fidelity screening approach will be developed, which integrates quantum mechanical simulations at different levels of fidelity with machine learning (ML) and database mining. ML algorithms will be used to analyze data generated by quantum mechanical simulations and to steer simulations for further data acquisition. High-cost high-fidelity evaluations of excited state properties of solid-state forms of candidate chromophores will be performed with many-body perturbation theory methods within the GW approximation and the Bethe-Salpeter equation. Lower-cost lower-fidelity evaluations of ground state features will be performed with density functional theory. Feature selection algorithms will then determine which descriptors are most predictive of the thermodynamic driving force for SF. These descriptors will be used to screen databases that contain crystal structures with no information or only partial information on their electronic properties. Optimization algorithms will be employed to decide which data points to sample and at what level of fidelity to maximize information gain. This research will advance the discovery of new intermolecular SF chromophores and will lead to advances in data science in the area of experimental design. The award is jointly funded through the Division of Materials Research and the Division of Mathematical Sciences in the Mathematical and Physical Sciences Directorate.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.

非技术摘要该奖项支持材料研究人员与数据科学家在 MATDAT18 数据马拉松活动中开展的持续合作。通过利用单线态裂变（SF）可以显着提高有机太阳能电池的效率，单线态裂变是一种量子力学过程，可以从一个光量子中产生两种导电物质，例如电子。目前，已知很少有材料在固态下表现出分子间SF，并且它们属于受限制的化学家族。 SF 尚未探索出大量可能制造的分子和晶体。 PI 将使用计算机模拟来寻找新型 SF 材料的多种可能性。为此，将开发一种新方法，该方法集成了量子力学模拟和机器学习领域的前沿进展。这项研究将推动材料科学和数据科学领域的发展。研究生和本科生将在计算材料科学和数据科学交叉领域的协作跨学科环境中接受培训，并获得高需求的可转移工作技能。技术摘要该奖项支持材料研究人员与数据科学家在 MATDAT18 数据马拉松活动中的持续合作。单线态裂变（SF）是将一个光生单线态激子转换为两个三线态激子。最近，人们对SF的兴趣激增，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高效地表现出分子间SF，这阻碍了固态SF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SF材料的计算发现，将开发一种新的多保真度筛选方法，该方法将不同保真度级别的量子力学模拟与机器学习（ML）和数据库挖掘相结合。机器学习算法将用于分析量子力学模拟生成的数据，并指导模拟以进行进一步的数据采集。将使用 GW 近似和 Bethe-Salpeter 方程内的多体微扰理论方法对候选发色团的固态形式的激发态特性进行高成本高保真度评估。将利用密度泛函理论对基态特征进行低成本、低保真度的评估。然后，特征选择算法将确定哪些描述符最能预测 SF 的热力学驱动力。这些描述符将用于筛选包含晶体结构的数据库，这些晶体结构没有有关其电子特性的信息或仅包含部分信息。将采用优化算法来决定对哪些数据点进行采样以及以何种保真度水平来最大化信息增益。这项研究将推动新的分子间 SF 发色团的发现，并将推动实验设计领域数据科学的进步。 该奖项由数学和物理科学理事会的材料研究部和数学科学部共同资助。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，认为值得支持。