Symmetrized density matrix renormalization group studies of the properties of low-lying states of the poly-para-phenylene system

Computational Approaches for Organic Semiconductors: From Chemical and Physical Understanding to Predicting New Materials

While a complete understanding of organic semiconductor (OSC) design principles remains elusive, computational methods─ranging from techniques based in classical and quantum mechanics to more recent data-enabled models─can complement experimental observations and provide deep physicochemical insights into OSC structure-processing-property relationships, offering new capabilities for in silico OSC discovery and design. In this Review, we trace the evolution of these computational methods and their application to OSCs, beginning with early quantum-chemical methods to investigate resonance in benzene and building to recent machine-learning (ML) techniques and their application to ever more sophisticated OSC scientific and engineering challenges. Along the way, we highlight the limitations of the methods and how sophisticated physical and mathematical frameworks have been created to overcome those limitations. We illustrate applications of these methods to a range of specific challenges in OSCs derived from π-conjugated polymers and molecules, including predicting charge-carrier transport, modeling chain conformations and bulk morphology, estimating thermomechanical properties, and describing phonons and thermal transport, to name a few. Through these examples, we demonstrate how advances in computational methods accelerate the deployment of OSCsin wide-ranging technologies, such as organic photovoltaics (OPVs), organic light-emitting diodes (OLEDs), organic thermoelectrics, organic batteries, and organic (bio)sensors. We conclude by providing an outlook for the future development of computational techniques to discover and assess the properties of high-performing OSCs with greater accuracy.

Excitation energy calculation of conjugated hydrocarbons: a new Pariser-Parr-Pople model parameterization approaching CASPT2 accuracy

A new parameterization for the Pariser-Parr-Pople (PPP) model for conjugated hydrocarbons is proposed in this work. The distance-dependence of PPP parameters are obtained from CASPT2 ground state and low-lying excited state energies of ethylene and its cation at various C-C single bond lengths and are fitted to a set of carefully chosen mathematical functions. Our new PPP model is applied to the calculation of vertical singlet-triplet energy gaps and the excitation energies for low-lying π→π(*) valence excitations in various π-conjugated molecules. Results with the new PPP model are consistently better than the standard PPP model in use. It often surpasses density functional theory and single-reference excited state methods such as configuration interaction singles or time-dependent density functional theory in terms of its accuracy and agrees reasonably well with high-level theories or experiments.