Alessandro Troisi

The model shows that OMIECs have high charge carrier mobility despite high disorder due to dynamic disorder at the timescale of charge transport being a significant fraction of overall disorder, parametrized with known OMIEC polymers.

Organic mixed ionic–electronic conductors (OMIECs) couple electronic doping to ion compensation and water reorganization. As doping increasing, molecular reorganization within the polymer–ion–water environment is thought to underlie the often nonmonotonic response of OMIEC devices, yet this process is essential for the understanding of structure–function relationship in related OMIEC devices but remains unclear. Although advanced operando measurements can resolve charge-state-dependent counterion localization within ordered OMIEC domains, jointly tracking electronic charging, explicit ion compensation, hydration, and molecular exchange across doping remains challenging; conventional simulations likewise do not allow these coupled variables to evolve together under constant electrochemical potential. To address these limitations under experimentally relevant electrochemical conditions, we develop an ion-regulated quantum mechanics/molecular mechanics (QM/MM) grand-canonical molecular dynamics framework that operates at constant electrochemical potential and samples OMIECs across experimentally relevant doping levels. Experimental 23Na and 35Cl nuclear magnetic resonance (NMR) spectra and layer-resolved computational sum-frequency-generation (SFG) spectra validate the polymer microstructure and polymer–electrolyte interfacial model. The simulations reveal a doping-dependent reorganization mechanism and identify a distinct compensation–exchange balance at the charging level associated with the experimental carrier-mobility maximum. With increasing doping, water–side-chain hydrogen bonding weakens and the glycol-rich environment crosses over from Na+ organization to Cl–-dominated compensation. Near the mobility maximum, persistent Na+ environments and a long-lived Na+–Cl– motif coexist with a more rapidly exchanging Cl– population. Further charging disrupts this balance, producing either kinetically trapped Cl– or a more highly hydrated, rapidly exchanging environment. These findings establish distinct ion-compensation and exchange regimes across doping and provide a general framework for resolving polymer–ion–water structure–function relationships in OMIECs.

We collected 218 metal-free organic RTP emitters and developed a Random Forest model combining physical/topological descriptors. It identified long-lifetime and long-wavelength RTP materials with accuracies of 0.774 and 0.820, yielding 65 candidates.

From a large database of 12 million commercially available molecules we identify those that can show emission from higher excited states and verify this property experimentally.

A fully automated, adaptive equilibration procedure designed for high-throughput simulations of polymer melts.

High-throughput virtual screening of molecules with suitable electronic properties rarely includes the computation of photophysical rate constants as target quantities. In this work, we develop a fully automated screening workflow capable of computing reverse intersystem crossing (RISC) rates for a large set of systems. The workflow is specifically designed to identify promising thermally activated delayed fluorescence (TADF) emitters and is applied to a medium-sized data set of 70 molecules. These candidates were selected from a database of 150,000 conjugated organic molecules not specifically tailored for TADF, based on desirable excited-state properties at the ground-state geometry (vertical singlet–triplet energy gap and oscillator strength). RISC rates were computed using a fully quantum-mechanical approach based on Fermi’s golden rule, which explicitly accounts for all nuclear degrees of freedom. The workflow autonomously performs the initial screening steps by excluding molecules that fail the required geometry optimization and frequency calculations and by diagnosing cases that cannot be reliably treated within the proposed framework, thereby ensuring robustness and reliability. Our results show that including RISC rate calculations drastically reduces the number of viable TADF candidates. This reduction arises from the combined effect of the key parameters governing the rate, mainly the adiabatic singlet–triplet energy gap and the reorganization energy.

The synthesis of thieno[3,2-b]thiophene bisquinoline derivatives via Pictet–Spengler reaction is reported, which were found to be highly versatile. Aromatic aldehydes afforded the desired products in moderate to good yields, with the observed reactivity depending strongly on the substitution pattern. In contrast, aliphatic aldehydes generated desired products only when the α-position of the benzylamine was blocked, while the open α-position yielded non-Pictet–Spengler reaction products, which is ascribed to aldehyde self-condensation or enol-mediated pathways.

This roadmap presents key advances and future directions in the field of the conducting polymer polyethylene dioxythiophene (PEDOT), with a focus on its role as multifunctional material in emerging electronic applications. It covers a broad spectrum of topics, including self-healing materials, plant bioelectronics, energy harvesting and storage, and neural interfaces. The roadmap also addresses recent progress in the synthesis and functionalization of PEDOT derivatives, as well as insights from molecular dynamics simulations and transport physics. Emphasis is placed on a range of processing techniques, such as electropolymerization, vapor phase polymerization, and 3D printing, and their implications for device performance. Specific applications explored include thermoelectrics, biosensors, organic electrochemical transistors, and advanced bioelectronic platforms for neuromodulation, stretchable electronics, and neuromorphic computing. This collective effort aims to serve as a comprehensive resource outlining the current state of PEDOT research and identifying challenges and opportunities for its integration across diverse electronic technologies.

High-throughput virtual screening (HTVS) is now routinely used for the discovery and rationalization of molecular materials with interesting excited-state properties for optoelectronics. While robust methods exist for the computation of energy levels for large datasets, it is more challenging to tackle more advanced properties related to the photophysics of the compounds. The challenge is related to both the complexity of the models and the difficulties in validating them against sufficiently large and homogeneous experiments. This work provides an overview of the challenges encountered when virtual screening approaches are used in organic electronics to study properties that are more advanced than just the energy levels of the system. We discuss how more advanced photophysical properties have begun to be incorporated into HTVS protocols, including excitation–vibration coupling and non-radiative rates. We examine how it is often necessary to go beyond the commonly used time-dependent density functional theory (TDDFT). We also provide examples of physical insights that can emerge from HTVS campaigns, highlighting the contribution that these screening approaches can give to fundamental understanding.

We present a machine learning approach (ΔML) capable of enhancing the accuracy of semiempirical excited-state energy calculations to a level close to that of Time-Dependent Density Functional Theory (TDDFT). Using a data set of 7600 organic π-conjugated molecules calculated at the ZINDO and M06-2X/3-21G* TDDFT computational levels, we trained a set of models to learn the systematic errors of the low-level method and correct it toward higher-level accuracy values. The best performing model improved the correlation of ZINDO S₁ energy predictions from 0.77 to 0.96 on a 9500 molecule test set of TDDFT target energies. Our ΔML-ZINDO model presents a negligible additional cost (∼2 ms per molecule) to a standard ZINDO calculation (∼2 s per molecule), enabling the computational screening of large data sets of molecules. Critical to the performance of the model is the AttentiveFP Message-Passing Neural Network with added electronic information derived from ZINDO calculations such as particle-hole densities. We also investigate the utility of the Morgan fingerprint and a novel descriptor designed to capture the electronic structure of molecules: a molecular orbital-weighted radial distribution function. The ΔML framework is retrainable to other low- and high-level calculation pairs, achieving an improvement in correlation from 0.88 to 0.99 on a test set of 24,000 molecules from the QCDGE data set, when mapping ZINDO to ωB97X-D/6-31G* energies. We also adapt ΔML-ZINDO for S₁ oscillator strength prediction, improving ZINDO predictions from a correlation of 0.524 to 0.839 on our M06-2X/3-21G* target test set, thus enabling the identification of emissive molecules.