Julien Gorenflot

Non‐fullerene acceptors (NFAs) have propelled organic photovoltaic efficiencies toward 21%, yet their modular acceptor–donor–acceptor chemistry creates a vast combinatorial design space that calls for efficient prescreening. Here we establish a computation‐driven screening workflow and apply it to over 4000 optimized A–B–(D–S)–B–A NFA molecules assembled from experimentally reported building blocks. Using a sequential set of device‐relevant criteria – suppressed dipole moment, a target quadrupole for efficient charge separation, trap‐free electron transport, sufficient interfacial energetic driving force, strong visible‐light absorption, and a sufficiently large photovoltaic gap – we downselect from over 4000 to the top 10 NFA candidates for common polymer donors (P3HT, PBDB‐T, PBDB‐T‐2F, and PBDB‐T‐SF). The top candidates recover established high‐performance motifs, including Y6‐type structures and an O‐IDTBR‐like candidate, supporting the validity of the descriptor set. Statistical analysis further reveals a clear division of control: donor building blocks primarily determine ionization energies, acceptor/end groups determine electron affinities, and the quadrupole moment provides a broadly tunable electrostatic handle that is only weakly correlated with frontier orbital energetics. Comparison to synthesized reference NFAs shows good agreement for thin‐film electronic and optical properties, while device performance highlights the importance of morphology beyond descriptor‐based prescreening.

Y-family acceptors (A) have enabled organic solar cell efficiencies above 21%, yet the origin of their outstanding performance remains unresolved. To identify the governing factors, we compare two Y-derivative acceptors, Y6 and ZY-4Cl, across binary blends with 11 different electron donors (D). We show that hole transfer (HT) at the D–A interface is the efficiency-limiting step, requiring an ionization energy offset of only ~0.3 eV for Y6 compared to ~0.6 eV for ZY-4Cl, set by interface energetics rather than π-stacking differences. Extending to an even wider range of systems, we find that external quantum efficiency universally scales with the Gibbs free energy for charge transfer, collapsing onto distinct trends defined by the strength of the acceptor’s electron deficient end group. Stronger acceptor units yield a steeper efficiency rise, enabling high external quantum efficiencies at minimal energetic cost, largely independent of molecular family and challenging the perceived advantage of Y-derivatives.

The bi-molecular recombination rate ( γ ) of charge carriers in P3HT:NFA blends decreases gradually as the diagonal bandgap (IE _D –EA _A ) increases.

Raman spectroscopy is a fast-growing and increasingly powerful analytical technique applied across diverse disciplines such as materials science, chemistry, biology and medicine. This growth is driven by advances in Raman instrumentation and greatly supported by the flourishing of chemometrics and artificial intelligence (AI). However, the full potential of this technique is often hampered by challenges related to data acquisition, processing, interpretation, and sharing. This review paper addresses how a concerted effort toward digitalization, incorporating principles of Open Science and FAIR data (Findable, Accessible, Interoperable, and Reusable), is essential to develop and implement robust, standardized, and accessible digital workflows. These workflows are key to unlock the full power of Raman spectroscopy in combination with AI. We explore the current landscape of digital tools and open resources in Raman spectroscopy, highlighting both existing solutions as well as critical gaps. Despite these advances, the field remains fragmented, with many initiatives developed in isolation, limiting interoperability and slowing progress. In this regard, we assess the trends in Raman spectroscopy hardware and control software as well as the role of AI in improving data collection, automating data analysis, extracting meaningful insights, and enabling predictive modeling. We review challenges such as data quality and model interpretability that constrain the effectiveness and applicability of AI in Raman spectroscopy. Furthermore, we emphasize the importance of standardized data formats, metadata schemas, and domain-specific ontologies to ensure machine-actionability, database federation and interoperability as well as to facilitate collaborative research. We provide curated lists of existing open hardware, databases and standards relevant to Raman spectroscopy. Finally, we propose a roadmap toward an open and FAIR ecosystem for Raman spectroscopy, emphasizing the need for sustainable infrastructure, collaborative development, and community involvement.

This study provides direct evidence that pristine L8BO exhibits interfacial charge generation, challenging prior Y6 studies on intrinsic free charge generation.

Raman spectroscopy is an increasingly powerful and fast-growing analytical technique across diverse disciplines, from materials science and chemistry to biology and medicine, thanks to advances in Raman instrumentation and greatly supported by the flourishing of chemometrics and artificial intelligence (AI). However, the full potential of this technique is often hampered by challenges related to data acquisition, processing, interpretation, and sharing. This review paper addresses how a concerted effort towards digitalization, incorporating principles of Open Science and FAIR data (Findable, Accessible, Interoperable, and Reusable), is essential to develop and implement robust, standardized, and accessible digital workflows in the field of Raman spectroscopy and thereby unlock the full power of Raman spectroscopy in combination with AI. We explore the current landscape of digital tools and open resources in Raman spectroscopy, highlight existing solutions as well as critical gaps. In this regard, we assess the trends in Raman spectroscopy hardware and control software as well as the role of artificial intelligence and machine learning in improving data collection, automating data analysis, extracting meaningful insights, and enabling predictive modelling. Furthermore, we discuss the importance of standardized data formats, metadata schemas, and ontologies to ensure database federation and interoperability as well as to facilitate collaborative research. We also provide lists of existing open hardware, open databases and standards. Finally, we propose a roadmap toward an open and FAIR ecosystem for Raman spectroscopy, emphasizing the need for sustainable infrastructure, collaborative development, and community involvement.

Non-fullerene based organic solar cells display a high initial power conversion efficiency but continue to suffer from poor thermal stability, especially in case of devices with thick active layers. Mixing of five structurally similar acceptors with similar electron affinities, and blending with a donor polymer is explored, yielding devices with a power conversion efficiency of up to 17.6%. The hexanary device performance is unaffected by thermal annealing of the bulk-heterojunction active layer for at least 23 days at 130 °C in the dark and an inert atmosphere. Moreover, hexanary blends offer a high degree of thermal stability for an active layer thickness of up to 390 nm, which is advantageous for high-throughput processing of organic solar cells. Here, a generic strategy based on multi-component acceptor mixtures is presented that permits to considerably improve the thermal stability of non-fullerene based devices and thus paves the way for large-area organic solar cells.

Molecular engineering of organic semiconductors provides a virtually unlimited number of possible structures, yet only a handful of combinations lead to state‐of‐the‐art efficiencies in photovoltaic applications. Thus, design rules that guide material development are needed. One such design principle is that in a bulk heterojunction consisting of an electron donor and lower bandgap acceptor an offset (ΔIE) of at least 0.45 eV is required between both materials ionization energies to overcome energy level bending at the donor–acceptor interface, in turn maximizing the charge separation yield and the cell's internal quantum efficiency. The present work studies energy losses associated with ΔIE and, based on 24 blends, finds that losses are minimal up to a ΔIE of 0.6 eV. Electroluminescence spectroscopy shows that low energy losses are achieved when the charge transfer state energy (E_CT) is similar to the acceptor's optical bandgap (E_g^A). Further ΔIE increase lowers E_CT with respect to E_g^A, thus decreasing V_OC. Within that 0.45–0.6 eV ΔIE sweet range, the fill factor FF, hence the power conversion efficiency, increases only marginally as the FF is often already close to maximal for ΔIE = 0.45 eV. The results are extended to 76 binary and ternary blends.

Non‐fullerene acceptor (NFA)‐based ternary bulk heterojunction solar cells (TSC) are the most efficient organic solar cells (OSCs) today due to their broader absorption and quantum efficiencies (QE) often surpassing those of corresponding binary blends. The impact on QE of the energetics driving charge transfer at the electron donor:electron acceptor (D/A) interfaces is studied in blends of PBDB‐T‐2F donor with several pairs of lower bandgap NFAs. As in binary blends, the ionization energy offset between donor and acceptor (ΔIE) controls the QE and maximizes for ΔIE > 0.5 eV. However, ΔIE is not controlled by the individual NFAs IEs but by their average, weighted for their blending ratio. Using this property, the QE of a PBDB‐T‐2F:IEICO binary blend that has an insufficient ΔIE for charge generation is improved by adding a deep IE third component: IT‐4F. Combining two NFAs enables to optimize the D/A energy alignment and cells’ QE without molecular engineering.

Non-fullerene acceptor (NFA)-based ternary bulk heterojunction solar cells (TSC) are the most efficient organic solar cells (OSCs) today due to their broader absorption and quantum efficiencies (QE) often surpassing those of corresponding binary blends. We study how the energetics driving charge transfer at the electron donor:electron acceptor (D/A) interfaces impact the QE in blends of PBDB-T-2F donor with several pairs of lower bandgap NFAs. As in binary blends, the ionization energy offset between donor and acceptor ({\Delta}IE) controls the QE and maximizes for {\Delta}IE > 0.5 eV. However, {\Delta}IE is not controlled by the individual NFAs IEs but by their average, weighted for their blending ratio. Using this property, we improved the QE of a PBDB-T-2F:IEICO binary blend that had an insufficient {\Delta}IE for charge generation by adding a deep IE third component: IT-4F. Combining two NFAs enables to optimize the D/A energy alignment and cells' QE without molecular engineering.