In this work, we present a thorough procedure for estimating the Flory-Huggins χ-parameter for use in atomistic and mesoscale molecular simulations in computational materials science. In particular, we propose improvements upon traditional Flory-Huggins theory by implementing a Connolly volume normalization (CVN). We apply this technique to several test systems, including a blend of poly (epichlorohydrin) and poly (methyl acrylate), a blend of polyethylene glycol and poly (methyl methacrylate), a blend of polystyrene and deuterated polystyrene, and three molecular-weight variants (monomer, dimer, and trimer) of a triblock copolymer for use in multicompartment micelle applications. Our results demonstrate that the newly developed procedure offers high accuracy and efficiency in predicting the Flory-Huggins χ-parameter for miscibility analysis compared to traditional experimental and computational methods. There are still several factors that cause the magnitude of the χ-parameter to vary between simulations performed on molecular species with the same identity but different degrees of polymerization; although we discuss possible explanations for these factors, this is nonetheless a primary focus for further exploration into this new methodology.

The advent of data analytics techniques and materials informatics provides opportunities to accelerate the discovery and development of organic semiconductors for electronic devices. However, the development of engineering solutions is limited by the ability to control thin-film morphology in an immense parameter space. The combination of highthroughput experimentation (HTE) laboratory techniques and data analytics offers tremendous avenues to traverse the expansive domains of tunable variables offered by organic semiconductor thin films. This Perspective outlines the steps required to incorporate a comprehensive informatics methodology into the experimental development of polymer-based organic semiconductor technologies. The translation of solution processing and property metrics to thin-film behavior is crucial to inform efficient HTE for data collection and application of data-centric tools to construct new process−structure−property relationships. We argue that detailed investigation of the solution state prior to deposition in conjunction with thin-film characterization will yield a deeper understanding of the physicochemical mechanisms influencing performance in π-conjugated polymer electronics, with data-driven approaches offering predictive capabilities previously unattainable via traditional experimental means.

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 effects of gamma irradiation on the dielectric and piezoelectric responses of Pb[Zr0.52Ti0.48]O3 (PZT) thin film stacks were investigated for structures with conductive oxide (IrO2) and metallic (Pt) top electrodes. The samples showed, generally, degradation of various key dielectric, ferroelectric, and electromechanical responses when exposed to 2.5 Mrad (Si) 60Co gamma radiation. However, the low-field, relative dielectric permittivity, εr, remained largely unaffected by irradiation in samples with both types of electrodes. Samples with Pt top electrodes showed substantial degradation of the remanent polarization and overall piezoelectric response, as well as pinching of the polarization hysteresis curves and creation of multiple peaks in the permittivity-electric field curves post irradiation. The samples with oxide electrodes, however, were largely impervious to the same radiation dose, with less than 5% change in any of the functional characteristics. The results suggest a radiation-induced change in the defect population or defect energy in PZT with metallic top electrodes, which substantially affects motion of internal interfaces such as domain walls. Additionally, the differences observed for stacks with different electrode materials implicate the ferroelectric–electrode interface as either the predominant source of radiation-induced effects (Pt electrodes) or the site of healing for radiation-induced defects (IrO2 electrodes).

Structural variation in multicompartment micelles consisting of lipophilic–hydrophilic–fluorophilic (hereafter referred to as BAC) triblock copolymers is investigated using the dissipative particle dynamics simulation method. It is demonstrated from our results that the structure of BAC multicompartment micelles is effectively tuned as a function of the lipophilic–fluorophilic ratio parameter, here termed , of the constituent linear triblock copolymers. In particular, a morphological deviation from onion-like ABC micelles arises in BAC micelle systems as increases. The morphologies of BAC micelles with or display striking similarities, with the only notable difference being an inversion of the lipophilic and fluorophilic regions. When , segmented worm-like structures with multiple cores are favored in BAC micelle systems. Through this study, it is confirmed that the block length ratio is an effective control parameter to tune the structure of multicompartment micelles.