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

Abstract

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 highperforming OSCs with greater accuracy.

The Quest for Air Stability in Organic Semiconductors

“…The relationship between dopants and OSCs thus far has been extensively studied for some dopants (e.g., F 4 TCNQ and N-DMBI) and OSCs [e.g., P3HT, PBTTT, and P­(NDI2OD-T2)] but limited for others, making it difficult to develop a broad understanding of how a dopant influences and modifies the morphology of OSCs. Further developments in this area, which is in its infancy, could derive from high-throughput computational studies to systematically and efficiently simulate the interactions of dopant–host pairs. − This would aid in a more extensive understanding of the relationship between the morphology and stabilities of doped OSCs. Doped OSCs are a crucial component for many organic electronic devices, including OLEDs, OPVs, OFETs, photodiodes, etc., that facilitates charge injection or extraction.…”

Perspectives on Development of Optoelectronic Materials in Artificial Intelligence Age

“…[9] However, the dramatic increase in computational cost incurred as the complexity of structural system increases poses a great challenge to further practical application. [10] Machine learning (ML), as a branch of artificial intelligence, is capable of dramatically lowering this computational cost. It can learn from existing data and generate a training model for predicting results outside of the training dataset, thus providing a promising approach for accelerating materials discovery (Figure 1 right).…”

“…Theoretical simulations mainly based on density functional theory (DFT) calculations that incorporate simplified model systems can capture the critical aspects of complex realistic systems and thus guide the rational design of new materials [9] . However, the dramatic increase in computational cost incurred as the complexity of structural system increases poses a great challenge to further practical application [10] . Machine learning (ML), as a branch of artificial intelligence, is capable of dramatically lowering this computational cost.…”

Benchmark Data Set of Crystalline Organic Semiconductors

“…In principle, the multiscale structural diversity of organic semiconductors offers great potential for fine-tuning their electronic properties. Nevertheless, achieving accurate and reliable results at a reasonable computational cost necessitates tailored modeling approaches that account for the complexity of their electronic structure and conformational motifs. , Even for a crystalline morphology, there are uncertainties with atomic positions and polymorph energy ranking because of the omnipresence of various types of disorder and external factors influencing intermolecular packing. , As a result, significant uncertainties emerge in computed properties, such as charge carrier mobility, as errors accumulate from multiple sources, including molecular geometry and force constant inaccuracies, electronic and vibronic couplings, and the accuracy of the electron–phonon Hamiltonian and its solution methods. Hence, the availability of a benchmark data set for organic semiconductors in their solid-state form is important for assessing the predictive capabilities of computational methods concerning the structural and electronic properties of these materials.…”