The 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.
The 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.
