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

非技术总结该奖项支持材料研究人员与MATDAT 18 Datasheet活动中点燃的数据科学家的持续合作。有机太阳能电池的效率可以通过利用单线态裂变（SF）来显著提高，单线态裂变是一种量子力学过程，其可以导致从一个量子光产生两个导电物质，例如电子。目前，已知很少有材料在固态下表现出分子间SF，并且它们属于受限的化学家族。大量可能的分子和晶体，可以作出尚未探索的SF。PI将使用计算机模拟来搜索新SF材料的许多可能性。为此，将开发一种新方法，该方法将量子力学模拟和机器学习的前沿进展整合在一起。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的界面上在协作的跨学科环境中进行培训，并获得高要求的可转移工作技能。该奖项支持材料研究人员与在MATDAT 18 Datasheet活动中点燃的数据科学家的持续合作。单重态裂变（SF）是一个光生单重态激子转化为两个三重态激子。最近，人们对SF的兴趣激增，这要归功于它通过从一个光子中捕获两个电荷载流子来显着提高有机太阳能电池效率的潜力。然而，目前已知很少有材料表现出高效率的分子间SF，阻碍了固态SF基太阳能电池的实现。可能的发色团的化合物空间是无限广阔的，而且在很大程度上是未开发的。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水平的量子力学模拟与机器学习（ML）和数据库挖掘相结合。ML算法将用于分析量子力学模拟生成的数据，并引导模拟以进一步获取数据。高成本的高保真度评价的激发态性质的固态形式的候选发色团将进行与多体微扰理论方法内的GW近似和Bethe-Salpeter方程。将用密度泛函理论对基态特征进行低成本、低保真度的评价。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，其中没有关于其电子性质的信息或只有部分信息。将采用优化算法来决定对哪些数据点进行采样，以及在什么样的保真度水平上最大限度地提高信息增益。这项研究将推动新的分子间SF生色团的发现，并将导致实验设计领域的数据科学的进步。 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。

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.