CAREER: Toward Reliable Nonadiabatic Dynamics in Condensed Matter and Nanoscale Systems

Alexey Akimov of the State University of New York at Buffalo is supported by a CAREER award from the Chemical Theory, Models and Computational Methods program in the Division of Chemistry for theoretical research aimed to advance the reliability and efficiency of quantum dynamics methods for condensed matter and nanoscale systems. Understanding the kinetics and mechanisms of quantum processes such as charge and energy transfer is critical for rationally designing a wide variety of materials such as photovoltaic, photocatalytic, or energy-storage systems, optoelectronic and quantum materials.

The computational modeling of the quantum processes in such complex systems at the atomistic level inevitably involves approximations that challenge the reliability of the computational predictions. The computational complexity also limits the range of the processes that can be studied from first principles. In this project, Akimov and his group will develop new theoretical frameworks, computational methodologies, and open-source software to lift the current limitations of quantum dynamics simulations in nanoscale systems.

This project will explore the quality of the presently-used simplified approaches, re-evaluate them from more rigorous grounds, and bring the state of quantum dynamics calculations in large systems to a new level of rigor and practicality, unreachable before. The open-source software developed in this project will enable researchers to study new, previously inaccessible, classes of solar energy materials, contributing toward sustainable and renewable energy economy development.

Dr. Akimov’s research program is closely integrated with his outreach and educational programs, including workshops on theoretical chemistry for graduate students and a virtual international seminar series for a broad scientific audience.

In this project, the Akimov group will develop and study reliable and efficient methods for modeling quantum nonadiabatic dynamics in condensed matter and nanoscale systems. The nonlinear dimensionality reduction and machine learning strategies will be explored to enable computing many nanosecond-long quantum trajectories, without expensive ab initio calculations.

These strategies are expected to help obtain the converged statistics of electronic transitions and estimate the error bars in computed properties. They will also enable modeling intrinsically slow quantum processes and infrequent events and help accelerate nonadiabatic dynamics modeling of large systems. The project aims to identify the “effective” low-dimensional coordinates that can be used to facilitate the analysis of photoexcited dynamics in various materials.

New techniques for nonadiabatic dynamics in large systems will be developed based on the formally-exact hierarchy of equations of motion method. These developments enable the accurate description of the bath-induced decoherence, and thermalization of excited states in complex systems, in a non-perturbative way. New nonadiabatic dynamics methods for computing electronic state couplings and energies at the many-body level, beyond the commonly-used single-particle picture, will be developed and implemented in open-source codes.

These advancements seek to enable computational material scientists to model excitonic dynamics in quantum-confined systems and capture slow dynamics with infrequent electronic transitions. The nonadiabatic dynamics approaches resulting from this project will be validated against the model and experimental references. These efforts aim to assess modern approximate quantum-classical methods for extended systems and bring them to a new level of rigor and reliability.

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.