Machine Learning Models Capture Plasmon Dynamics in Ag Nanoparticles.

Highly energetic electron-hole pairs (hot carriers) formed from plasmon decay in metallic nanostructures promise sustainable pathways for energy-harvesting devices. However, efficient collection before thermalization remains an obstacle for realization of their full energy generating potential. Addressing this challenge requires detailed understanding of physical processes from plasmon excitation in the metal to their collection in a molecule or a semiconductor, where atomistic theoretical investigation may be particularly beneficial. Unfortunately, first-principles theoretical modeling of these processes is extremely costly, preventing a detailed analysis over a large number of potential nanostructures and limiting the analysis to systems with a few 100s of atoms. Recent advances in machine learned interatomic potentials suggest that dynamics can be accelerated with surrogate models which replace the full solution of the Schrödinger Equation. Here, we modify an existing neural network, Hierarchically Interacting Particle Neural Network (HIP-NN), to predict plasmon dynamics in Ag nanoparticles. The model takes as a minimum as three time steps of the reference real-time time-dependent density functional theory (rt-TDDFT) calculated charges as history and predicts trajectories for 5 fs in great agreement with the reference simulation. Further, we show that a multistep training approach in which the loss function includes errors from future time-step predictions can stabilize the model predictions for the entire simulated trajectory (∼25 fs). This extends the model's capability to accurately predict plasmon dynamics in large nanoparticles of up to 561 atoms, not present in the training data set. More importantly, with machine learning models on GPUs, we gain a speed-up factor of ∼103 as compared with the rt-TDDFT calculations when predicting important physical quantities such as dynamic dipole moments in Ag55 and a factor of ∼104 for extended nanoparticles that are 10 times larger. This underscores the promise of future machine learning accelerated electron/nuclear dynamics simulations for understanding fundamental properties of plasmon-driven hot carrier devices.

Giant Spin Lifetime Anisotropy and Spin-Valley Locking in Silicene and Germanene from First-Principles Density-Matrix Dynamics.

Through first-principles real-time density-matrix (FPDM) dynamics simulations, we investigated spin relaxation due to electron-phonon and electron-impurity scatterings with spin-orbit coupling (SOC) in two-dimensional Dirac materials silicene and germanene at finite temperatures. We discussed the applicability of conventional descriptions of spin relaxation mechanisms by Elliott-Yafet (EY) and D'yakonov-Perel' (DP) compared to the FPDM method, which is determined by a complex interplay of intrinsic SOC, external fields, and scattering strength. For example, the electric field dependence of the spin lifetime by FPDM is close to the DP mechanism for silicene at room temperature but similar to the EY mechanism for germanene. Because of its stronger SOC strength and buckled structure in contrast to graphene, germanene has a giant spin lifetime anisotropy and spin-valley locking effect under nonzero Ez and low temperatures. More importantly, germanene has a long spin lifetime (∼100 ns at 50 K) and an ultrahigh carrier mobility, making it advantageous for spin-valleytronic applications.

Ultrafast hot-hole injection modifies hot-electron dynamics in Au/p-GaN heterostructures.

A fundamental understanding of hot-carrier dynamics in photo-excited metal nanostructures is needed to unlock their potential for photodetection and photocatalysis. Despite numerous studies on the ultrafast dynamics of hot electrons, so far, the temporal evolution of hot holes in metal-semiconductor heterostructures remains unknown. Here, we report ultrafast (t < 200 fs) hot-hole injection from Au nanoparticles into the valence band of p-type GaN. The removal of hot holes from below the Au Fermi level is observed to substantially alter the thermalization dynamics of hot electrons, reducing the peak electronic temperature and the electron-phonon coupling time of the Au nanoparticles. First-principles calculations reveal that hot-hole injection modifies the relaxation dynamics of hot electrons in Au nanoparticles by modulating the electronic structure of the metal on timescales commensurate with electron-electron scattering. These results advance our understanding of hot-hole dynamics in metal-semiconductor heterostructures and offer additional strategies for manipulating the dynamics of hot carriers on ultrafast timescales.

Coupled Electromagnetic and Reaction Kinetics Simulation of Super-Resolution Interference Lithography.

Inspired by the ability of super-resolved fluorescence microscopy to circumvent the diffraction barrier, two-color super-resolution interference lithography exploits nonequilibrium kinetics in materials to achieve large-area nanopatterning while using visible light. Periodic patterns with super-resolved features down to tens of nanometers have been demonstrated in thin films and monolayers. Extending these advances to the bulk nanopatterning of thick films requires a quantitative understanding of the time-dependent interactions of optical dynamics, including absorption, diffraction, and intensity modulation at two wavelengths, with the photoactivated and inhibited reaction kinetics. Here, we develop an efficient electromagnetic (EM) perturbation theory approach that facilitates for the first time fully coupled simulations of EM and chemical kinetics in two-color interference lithography. Applied to a spirothiopyran-functionalized photoresist system, these simulations show that diffraction and absorption effects are negligible (<0.1%) for depths up to 10 µm, and that tuning exposure time and intensities can lead to concentration contrast up to 80%. We investigate multiple exposure strategies to reduce the pitch of the line pattern including sequential exposures with different times to achieve uniform lines and multiplexed exposures with equal periods. This capability to rapidly and accurately predict the coupled optical and chemical dynamics facilitates the computational design of high-precision patterns in two-color interference lithography.

Hot-Hole versus Hot-Electron Transport at Cu/GaN Heterojunction Interfaces.

Among all plasmonic metals, copper (Cu) has the greatest potential for realizing optoelectronic and photochemical hot-carrier devices, thanks to its CMOS compatibility and outstanding catalytic properties. Yet, relative to gold (Au) or silver (Ag), Cu has rarely been studied and the fundamental properties of its photoexcited hot carriers are not well understood. Here, we demonstrate that Cu nanoantennas on p-type gallium nitride (p-GaN) enable hot-hole-driven photodetection across the visible spectrum. Importantly, we combine experimental measurements of the internal quantum efficiency (IQE) with ab initio theoretical modeling to clarify the competing roles of hot-carrier energy and mean-free path on the performance of hot-hole devices above and below the interband threshold of the metal. We also examine Cu-based plasmonic photodetectors on corresponding n-type GaN substrates that operate via the collection of hot electrons. By comparing hot hole and hot electron photodetectors that employ the same metal/semiconductor interface (Cu/GaN), we further elucidate the relative advantages and limitations of these complementary plasmonic systems. In particular, we find that harnessing hot holes with p-type semiconductors is a promising strategy for plasmon-driven photodetection across the visible and ultraviolet regimes. Given the technological relevance of Cu and the fundamental insights provided by our combined experimental and theoretical approach, we anticipate that our studies will have a broad impact on the design of hot-carrier optoelectronic devices and plasmon-driven photocatalytic systems.

Spin-phonon relaxation from a universal ab initio density-matrix approach.

Designing new quantum materials with long-lived electron spin states urgently requires a general theoretical formalism and computational technique to reliably predict intrinsic spin relaxation times. We present a new, accurate and universal first-principles methodology based on Lindbladian dynamics of density matrices to calculate spin-phonon relaxation time of solids with arbitrary spin mixing and crystal symmetry. This method describes contributions of Elliott-Yafet and D'yakonov-Perel' mechanisms to spin relaxation for systems with and without inversion symmetry on an equal footing. We show that intrinsic spin and momentum relaxation times both decrease with increasing temperature; however, for the D'yakonov-Perel' mechanism, spin relaxation time varies inversely with extrinsic scattering time. We predict large anisotropy of spin lifetime in transition metal dichalcogenides. The excellent agreement with experiments for a broad range of materials underscores the predictive capability of our method for properties critical to quantum information science.