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We investigate a new strategy to enhance thermally activated delayed fluorescence (TADF) in organic light-emitting diodes (OLEDs). Given that the TADF rate of a molecule depends on its conformation, we hypothesize that there exists a conformation that maximizes the TADF rate. To test this idea, we use time-dependent density functional theory (TDDFT) to simulate the TADF rates of several TADF emitters while varying their geometries in a select subspace of internal coordinates. We find that geometric changes in this subspace can increase the TADF rate up to 3 orders of magnitude with respect to the minimum energy conformation, and the simulated TADF rate can even be brought into the submicrosecond time scales under the right conditions. Furthermore, the TADF rate enhancement can be maintained with a conformational energy that might be within the reach of modern synthetic chemistry. Analyzing the maximum TADF conformation, we extract a number of structural motifs that might provide a useful handle on the TADF rate of a donor-acceptor (DA) system. The incorporation of conformational engineering into the TADF technology could usher in a new paradigm of OLEDs.

Lattice models are a useful tool to simulate the kinetics of surface reactions. Since it is expensive to propagate the probabilities of the entire lattice configurations, it is practical to consider the occupation probabilities of a typical site or a cluster of sites instead. This amounts to a moment closure approximation of the chemical master equation. Unfortunately, simple closures, such as the mean-field and the pair approximation (PA), exhibit weaknesses in systems with significant long-range correlation. In this paper, we show that machine learning (ML) can be used to construct accurate moment closures in chemical kinetics using the lattice Lotka-Volterra model as a model system. We trained feedforward neural networks on kinetic Monte Carlo (KMC) results at select values of rate constants and initial conditions. Given the same level of input as PA, the ML moment closure (MLMC) gave accurate predictions of the instantaneous three-site occupation probabilities. Solving the kinetic equations in conjunction with MLMC gave drastic improvements in the simulated dynamics and descriptions of the dynamical regimes throughout the parameter space. In this way, MLMC is a promising tool to interpolate KMC simulations or construct pretrained closures that would enable researchers to extract useful insight at a fraction of the computational cost.

Lattice models are a useful tool to simulate the kinetics of surface reactions. Since it is expensive to propagate the probabilities of the entire lattice configurations, it is practical to consider the occupation probabilities of a typical site or a cluster of sites instead. This amounts to a moment closure approximation of the chemical master equation. Unfortunately, simple closures, such as the mean-field and the pair approximation (PA), exhibit weaknesses in systems with significant long-range correlation. In this paper, we show that machine learning (ML) can be used to construct accurate moment closures in chemical kinetics using the lattice Lotka-Volterra model as a model system. We trained feedforward neural networks on kinetic Monte Carlo (KMC) results at select values of rate constants and initial conditions. Given the same level of input as PA, the ML moment closure (MLMC) gave accurate predictions of the instantaneous three-site occupation probabilities. Solving the kinetic equations in conjunction with MLMC gave drastic improvements in the simulated dynamics and descriptions of the dynamical regimes throughout the parameter space. In this way, MLMC is a promising tool to interpolate KMC simulations or construct pretrained closures that would enable researchers to extract useful insight at a fraction of the computational cost.

We introduce a simple quantum-mechanical model for thermally activated delayed fluorescence (TADF). The Hamiltonian is represented in the basis of four spin-mixed diabatic states representing pure charge transfer (CT) and local excitations (LE). The model predicts that it is possible to realize lowest-lying adiabatic singlet (S1) and triplet (T1) states with a small singlet-triplet gap, differing CT/LE contributions, and appreciable LE component in the S1 state. These characteristics can explain the coexistence of fast T1 → S1 reverse intersystem crossing and S1 → S0 radiative decay in some chromophores. Through the sampling of the parameter space and statistical analysis of the data, we show which parameter combinations contribute the most to the TADF efficiency. We also show that conformational fluctuations of a single model donor-acceptor system sample a significant region of the parameter space and can enhance the TADF rate by almost 3 orders of magnitude. This study provides new guidelines for optimization of TADF emitters by means of electronic structure and conformation engineering., United States. Department of Energy. Office of Basic Energy Sciences (Grant BES ER46474)

We introduce a simple quantum-mechanical model for thermally activated delayed fluorescence (TADF). The Hamiltonian is represented in the basis of four spin-mixed diabatic states representing pure charge transfer (CT) and local excitations (LE). The model predicts that it is possible to realize lowest-lying adiabatic singlet (S1) and triplet (T1) states with a small singlet–triplet gap, differing CT/LE contributions, and appreciable LE component in the S1 state. These characteristics can explain the coexistence of fast T1 → S1 reverse intersystem crossing and S1 → S0 radiative decay in some chromophores. Through the sampling of the parameter space and statistical analysis of the data, we show which parameter combinations contribute the most to the TADF efficiency. We also show that conformational fluctuations of a single model donor–acceptor system sample a significant region of the parameter space and can enhance the TADF rate by almost 3 orders of magnitude. This study provides new guidelines for optimization of TADF emitters by means of electronic structure and conformation engineering.