RUI: D3SC: Advancement of Excitation Energy Transfer Modeling with Deep Learning Algorithms

Monmouth University, West Long Branch NJ

Investigators

Abstract

Dmitri Kosenkov of Monmouth University is supported by an award from the Chemical Theory, Models and Computational Methods program in the Division of Chemistry to advance computational methods for modeling electronic excitation energy transfer dynamics in complex molecular systems by the incorporation of deep learning techniques. Excitation energy transfer is a fundamentally important process for light harvesting and utilization in natural and artificial systems. Modern technologies, such as photovoltaics and molecular sensors, involve energy transfer. In particular, understanding excitation energy transfer mechanisms in molecular fluorescent probes is crucial for targeting proteins, DNA sequences, and sensing of intracellular environments. The challenges in modeling excitation energy transfer processes is associated with the large sizes and complex structures of the molecular systems. Professor Kosenkov and coworkers are incorporating machine learning into computational chemistry techniques to overcome these obstacles. This approach may accelerate computations and reduce computational cost. The scientific knowledge has the potential to advance in the fields of chemical biology, artificial photosynthesis, organic photovoltaics, and phototherapies. Professor Kosenkov involves undergraduate students at Monmouth University, a primarily undergraduate institution, in his research. The Kosenkov group disseminates the computational methods to the broader scientific community via computational chemistry software and research-based laboratory projects that can be implemented in colleges and universities in the United States and worldwide. Finally, the work supports computational chemistry workshops for undergraduate and high school students at Monmouth University, where leading scientists in the fields of computational chemistry, chemical biology, and machine learning share their knowledge and experience. The research is focused on novel bioorthogonal boron dipyrromethene-derived probes that offer a revolutionary tool for visualization of cell organelles, intracellular drug distribution, and open a possibility for potential phototherapy through controlled generation of cytotoxic singlet oxygen under light activation. Excitation energy transfer is a key process for operation of these molecular fluorescent probes. Professor Kosenkov and his collaborators are developing an original computational methodology that integrates quantum dynamics theories with deep learning algorithms. The dynamics of energy transfer is modeled with the quantum master equation methods. The deep learning component enables high-throughput screening of electronic excitation energies and couplings further used in quantum dynamics simulations. The methodology is advantageous due to its flexibility and applicability to a wide range of molecular systems. The methods enable elucidation and comparison of the mechanisms of excitation energy transfer for molecular fluorescent probes of varied chemical structures. Moreover, the software tools simulate a wide range of molecular systems and may allow for further iterative improvements as the field develops. The educational component is focused on development and dissemination of a sequence of research-based multi-week computational laboratory units, primarily for physical and computational chemistry courses. These laboratory units cover basics of deep leaning algorithms and their applications in chemistry including modeling of fluorescent probes. 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.