Efficient intersystem crossing (ISC) and reverse ISC
(RISC) processes
are of vital significance for thermally activated delayed fluorescence
(TADF) materials to achieve 100% internal quantum efficiency. However,
it is challenging to rapidly predict the ISC/RISC rates of large amounts
of TADF materials and screen promising candidates because of their
flexible molecular design. Here, we perform virtual screening of 564
candidates constructed from 20 unique building blocks linking in D–A,
D−π–A, and D–A–D (D′) configurations
using the established machine learning models of GBRT and RF-GBRT-KNN
with the Pearson’s correlation coefficients (r) of 0.89 and 0.82, respectively. Novel descriptors of ΔE
LL, Polar, and ΔE
TT for predicting ISC/RISC rates were proposed,
and nine TADF molecules with the predicted ISC and RISC rates of >7
× 107 and 2 × 105 s–1, respectively, were revealed. We provide an efficient approach to
predicting ISC and RISC rates of TADF molecules on a large scale,
elucidating important building blocks and architectures to design
high-performance optoelectronic materials for experimental explorations.