Ab Initio Molecular Cavity Quantum Electrodynamics Simulations Using Machine Learning Models

We present a mixed quantum-classical simulation of polariton dynamics for molecule–cavity hybrid systems. In particular, we treat the coupled electronic–photonic degrees of freedom (DOFs) as the quantum subsystem and the nuclear DOFs as the classical subsystem and use the trajectory surface hopping approach to simulate non-adiabatic dynamics among the polariton states due to the coupled motion of nuclei. We use the accurate nuclear gradient expression derived from the Pauli–Fierz quantum electrodynamics Hamiltonian without making further approximations. The energies, gradients, and derivative couplings of the molecular systems are obtained from the on-the-fly simulations at the level of complete active space self-consistent field (CASSCF), which are used to compute the polariton energies and nuclear gradients. The derivatives of dipoles are also necessary ingredients in the polariton nuclear gradient expression but are often not readily available in electronic structure methods. To address this challenge, we use a machine learning model with the Kernel ridge regression method to construct the dipoles and further obtain their derivatives, at the same level as the CASSCF theory. The cavity loss process is modeled with the Lindblad jump superoperator on the reduced density of the electronic–photonic quantum subsystem. We investigate the azomethane molecule and its photoinduced isomerization dynamics inside the cavity. Our results show the accuracy of the machine-learned dipoles and their usage in simulating polariton dynamics. Our polariton dynamics results also demonstrate the isomerization reaction of azomethane can be effectively tuned by coupling to an optical cavity and by changing the light–matter coupling strength and the cavity loss rate

Analysis of the Geometrical Evolution in On-the-Fly Surface-Hopping Nonadiabatic Dynamics with Machine Learning Dimensionality Reduction Approaches: Classical Multidimensional Scaling and Isometric Feature Mapping

On-the-fly trajectory-based nonadiabatic dynamics simulation has become an important approach to study ultrafast photochemical and photophysical processes in recent years. Because a large number of trajectories are generated from the dynamics simulation of polyatomic molecular systems with many degrees of freedom, the analysis of simulation results often suffers from the large amount of high-dimensional data. It is very challenging but meaningful to find dominating active coordinates from very complicated molecular motions. Dimensionality reduction techniques provide ideal tools to realize this purpose. We apply two dimensionality reduction approaches (classical multidimensional scaling and isometric feature mapping) to analyze the results of the on-the-fly surface-hopping nonadiabatic dynamics simulation. Two representative model systems, CH₂NH₂⁺ and the phytochromobilin chromophore model, are chosen to examine the performance of these dimensionality reduction approaches. The results show that these approaches are very promising, because they can extract the major molecular motion from complicated time-dependent molecular evolution without preknown knowledge

Inclusion of Machine Learning Kernel Ridge Regression Potential Energy Surfaces in On-the-Fly Nonadiabatic Molecular Dynamics Simulation

We discuss a theoretical approach that employs machine learning potential energy surfaces (ML-PESs) in the nonadiabatic dynamics simulation of polyatomic systems by taking 6-aminopyrimidine as a typical example. The Zhu–Nakamura theory is employed in the surface hopping dynamics, which does not require the calculation of the nonadiabatic coupling vectors. The kernel ridge regression is used in the construction of the adiabatic PESs. In the nonadiabatic dynamics simulation, we use ML-PESs for most geometries and switch back to the electronic structure calculations for a few geometries either near the S₁/S₀ conical intersections or in the out-of-confidence regions. The dynamics results based on ML-PESs are consistent with those based on CASSCF PESs. The ML-PESs are further used to achieve the highly efficient massive dynamics simulations with a large number of trajectories. This work displays the powerful role of ML methods in the nonadiabatic dynamics simulation of polyatomic systems

Inclusion of Machine Learning Kernel Ridge Regression Potential Energy Surfaces in On-the-Fly Nonadiabatic Molecular Dynamics Simulation

We discuss a theoretical approach that employs machine learning potential energy surfaces (ML-PESs) in the nonadiabatic dynamics simulation of polyatomic systems by taking 6-aminopyrimidine as a typical example. The Zhu–Nakamura theory is employed in the surface hopping dynamics, which does not require the calculation of the nonadiabatic coupling vectors. The kernel ridge regression is used in the construction of the adiabatic PESs. In the nonadiabatic dynamics simulation, we use ML-PESs for most geometries and switch back to the electronic structure calculations for a few geometries either near the S₁/S₀ conical intersections or in the out-of-confidence regions. The dynamics results based on ML-PESs are consistent with those based on CASSCF PESs. The ML-PESs are further used to achieve the highly efficient massive dynamics simulations with a large number of trajectories. This work displays the powerful role of ML methods in the nonadiabatic dynamics simulation of polyatomic systems

Computational Investigation of Acene-Modified Zinc-Porphyrin Based Sensitizers for Dye-Sensitized Solar Cells

A series of acene-modified zinc-porphyrin dyes (benzene to pentacene, denoted as LAC-1 to LAC-5) were chosen to examine their performance as photosensitizers in dye-sensitized solar cells (DSSCs). Their structural, electronic, and optical properties were investigated at the DFT/TDDFT levels using various theoretical models (i.e., the gas phase model and the implicit/explicit solvent model). The dye@TiO₂ complex was used to investigate the dye/semiconductor interfaces using both the cluster and periodic models. After a careful examination of the dependence of the results on different theoretical approaches, some basic principles could be derived based on the theoretical investigation of structure–function relationships in isolated dyes and dye–TiO₂ assemblies. Based on these ideas, some general suggestions can be proposed for the future design of dyes for use in DSSCs. For instance, the DFT functionals used in estimating the critical parameters for DSSCs should be carefully validated. Sometimes the performances of the DFT functionals can be improved by a specific energy-shift correction to compensate for systematic errors. Benchmark calculations indicated that the best approach for depicting the reduction potentials is either the M06-2X functional combined with the formula Δ<i>E</i>_red = (<i>E</i>⁰ – <i>E</i>^–)_GS or the B3LYP functional combined with Koopman’s Theorem. The best functional for estimating the excitation energies was found to be LC-ωPBE. The impact of significant thermal fluctuations on the optoelectronic properties of dyes may also be an important consideration in the prediction of more efficient dyes for use DSSCs. In contrast to the selection of DFT functionals, both the cluster and periodic models resulted in consistent views of the dye–TiO₂ interactions, indicating that the use of either model should achieve reasonable results at least in the qualitative manner

Rigid–Flexible Coupling High Ionic Conductivity Polymer Electrolyte for an Enhanced Performance of LiMn₂O₄/Graphite Battery at Elevated Temperature

LiMn₂O₄-based batteries exhibit severe capacity fading during cycling or storage in LiPF₆-based liquid electrolytes, especially at elevated temperatures. Herein, a novel rigid–flexible gel polymer electrolyte is introduced to enhance the cyclability of LiMn₂O₄/graphite battery at elevated temperature. The polymer electrolyte consists of a robust natural cellulose skeletal incorporated with soft segment poly­(ethyl α-cyanoacrylate). The introduction of the cellulose effectively overcomes the drawback of poor mechanical integrity of the gel polymer electrolyte. Density functional theory (DFT) calculation demonstrates that the poly­(ethyl α-cyanoacrylate) matrices effectively dissociate the lithium salt to facilitate ionic transport and thus has a higher ionic conductivity at room temperature. Ionic conductivity of the gel polymer electrolyte is 3.3 × 10^–3 S cm^–1 at room temperature. The gel polymer electrolyte remarkably improves the cycling performance of LiMn₂O₄-based batteries, especially at elevated temperatures. The capacity retention after the 100th cycle is 82% at 55 °C, which is much higher than that of liquid electrolyte (1 M LiPF₆ in carbonate solvents). The polymer electrolyte can significantly suppress the dissolution of Mn²⁺ from surface of LiMn₂O₄ because of strong interaction energy of Mn²⁺ with PECA, which was investigated by DFT calculation