Abstract

Engineering next-generation electronic devices, ranging from battery electrodes, photoelectrocatalysts, to solid-state quantum sensors, requires precise knowledge of how the structural arrangement of atoms impact the electronic properties of materials during synthesis and device operation. A promising tool for studying this is first-principles molecular dynamics (FPMD) based on density functional theory. However, these simulations are computationally expensive, which hinders their application. In this talk, I will discuss a computational modeling framework that combines FPMD with enhanced sampling and machine learning, to reveal electron spins and molecular reactions, as materials undergo electronic and structural changes. The first part will focus on the development of a neural network approach in simulating reactions in condensed phase systems. These algorithms enable the simulation of reactive processes, for instance, in the molecular nitrogen dissociation on metal catalysts, which is the rate-determining step in ammonia synthesis [1]. In the second part, I will discuss modeling the high-temperature formation of quantum defects in solids to realize scalable quantum systems. This study reveals that understanding electronic structure and dynamics of spin defects are keys to designing new quantum technologies [2].

[1] E. M.Y. Lee, T. Ludwig, B. Yu, A. Singh, F. Gygi, J. K. Norskov, J. J. de Pablo. J. Phys. Chem. Letters 12, 2954-2962 (2021)

[2] E. M.Y. Lee, A. Yu, J. J. de Pablo, and G. Galli. Nature Communications, 12, 6325, 2021