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

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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.