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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Quantifying the impact of operating temperature on cracking in battery electrodes, using super-resolution of microscopy images and stereology

There are numerous factors that can have an impact on the degradation behavior of batteries, such as the number of recharge cycles or the charge rate. Here, we investigate the influence of operating temperature on the structural degradation of the microstructure in lithium-ion positive electrodes. For that purpose, the microstructure is characterized for cathodes which have been cycled for 200 cycles under 6C (10-minute) charging at different operating temperatures, namely, 20°C, 30°C, 40°C, and 50°C. For each operating condition scanning electron microscopy (SEM) images of cross-sectioned Li x Ni 0.5 Mn 0.3 Co 0.2 O 2 (NMC532) electrodes have been analyzed, to determine structural descriptors such as global particle porosity, crack size/length/width distribution, and porosity and specific surface area distribution of individual particles. Additionally, a stereological method has been deployed to investigate the local particle porosity as a function of distance to the particle center. Results show that particle porosity increases with increasing cycling temperature. Particle porosity is greatest at the particle center and decreases along the particle radius to the exterior. Particle surface area is similar across the four cycling-temperature aging conditions.

25 ENERGY STORAGE↗
DOE OSTI2024

Uncertainty quantification and propagation in lithium-ion battery electrodes using bayesian convolutional neural networks

The complex nature of manufacturing processes stipulates electrodes to possess high variability with increased heterogeneity during production. X-ray computed tomography imaging has proved to be critical in visualizing the complicated stochastic particle distribution of as-manufactured electrodes in lithium-ion batteries. However, accurate prediction of their electrochemical performance necessitates precise evaluation of kinetic and transport properties from real electrodes. Image segmentation that characterizes voxels to particle/pore phase is often meticulous and fraught with subjectivity owing to a myriad of unconstrained choices and filter algorithms. Here we utilize a Bayesian convolutional neural network to tackle segmentation subjectivity and quantify its pertinent uncertainties. Otsu inter-variance and Blind/Referenceless Imaging Spatial Quality Evaluator are used to assess the relative image quality of grayscale tomograms, thus evaluating the uncertainty in the derived microstructural attributes. We analyze how image uncertainty is correlated with the uncertainties and magnitude of kinetic and transport properties of an electrode, further identifying pathways of uncertainty propagation within microstructural attributes. The coupled effect of spatial heterogeneity and microstructural anisotropy on the uncertainty quantification of transport parameters is also understood. This work demonstrates a novel methodology to extract microstructural descriptors from real electrode images through quantification of associated uncertainties and discerning the relative strength of their propagation, thus facilitating feedback to manufacturing processes from accurate image based electrochemical simulations.

25 ENERGY STORAGE↗
DOE OSTI2025

Non-destructive electrochemical diagnosis of failure mechanisms in aqueous zinc batteries

The early detection of secondary reactions that affect the life and performance of zinc manganese oxide batteries requires a shift from conventional time-consuming and often destructive procedures to rapid lifetime-predictive techniques. In this work, an electrochemical approach is employed to elucidate independent signatures for four common types of failure mechanisms in zinc manganese dioxide (Zn||MnO2) batteries—namely, the loss of zinc inventory, the loss of active material at the cathode, electrolyte depletion, and increased cell impedance. Our findings, specific to coin cell configurations, reveal that each induced failure mechanism can be distinctively modeled and identified based on responses from the rest voltage and columbic-efficiency data for prompt detection. For instance, electrolyte depletion response manifests a distinctive abrupt (>80 %) decrease in columbic efficiency (CE) and charge-rest voltage (Vc) while the discharge-rest voltage remained constant at ~1.3 V. Furthermore, electrolyte rejuvenation of the cell increased the CE to >95 % and restored Vc from ~0.3 to >1.7 V. Recovery experiments and reference performance tests demonstrated consistency between electrochemical descriptors and their associated failure mechanisms. Further, the outcomes of this work provide valuable insights and data models for some of the dominant failure mechanisms present in zinc manganese battery chemistries, which are beneficial to accelerated early-lifetime diagnosis and advancement of Zn batteries development.

25 ENERGY STORAGE↗
DOE OSTI2026

Physics-informed machine learning exploration of Na storage mechanisms in disordered carbon

Sodium-ion batteries are a cost-effective, sustainable alternative to lithium-ion systems for large-scale energy storage. However, optimizing sodium storage in carbon-based anodes with microstructural complexity and atomic disorder remains a major challenge. The intrinsic inhomogeneity of these materials produces diverse local environments, making it difficult for conventional methods to predict and control ion dynamics. Hard carbon (HC) anodes, composed of ranges of ordered-to-disordered graphitic and amorphous nanodomains, offer tunable ion storage and rate capacity, yet rationale design remains a challenge due to poorly understood correlation between local atomic feature and ion transport mechanism. Here, to address this challenge, we introduce a data-driven framework that integrates validated machine-learned interatomic potentials, large-scale molecular dynamics simulations, and machine learning to elucidate sodium transport mechanisms as a function of carbon and sodium loading densities. By computing per-ion structural descriptors and applying unsupervised learning, we identify distinct diffusion modes governed by microscopic features. Supervised analysis and correlation mapping then establish quantitative links between these transport regimes and processing variables such as bulk carbon density and sodium content. This physics-informed approach establishes quantitative structure–transport relationships and offers actionable design principles for engineering high-performance HC anodes.

Data-driven framework↗
DOE OSTI2022

Biotic predictors improve species distribution models for invasive plants in Western U.S. Forests at high but not low spatial resolutions

Invasions by non-native plants threaten forest health and sustainability. The ability to predict areas at greatest risk to invasion is essential for informing both monitoring and management of invasive species. Species distribution models (SDMs) are often used to identify environmental correlates of a species’ occurrence and predict geographic areas that may be suitable for its presence and are commonly constructed using solely abiotic predictors. However, mounting evidence implies that not including biotic predictors in SDMs may yield less accurate models at some resolutions typical of landscape-scale models, although this possibility has rarely been evaluated in invasive plants. In this study, we determined whether including descriptors of the biotic environment improved the accuracy of SDMs built at five decreasing spatial resolutions for infestations of five common invasive plants in forests of California, Oregon, and Washington, USA and described environmental correlates of each species’ presence. Predictors of occurrence often echoed those identified in previous studies of the focal species, indicating that our models accurately identified important environmental drivers of occurrence. Including biotic predictors in the SDMs consistently improved model accuracy only at the highest resolution we examined, which may be due to the spatial scale at which biotic interactions primarily act to constrain species’ distributions, the particular biotic predictors we used in our models, or correlations between attributes of the abiotic and biotic environment. This finding suggests that, while the practice of building SDMs using abiotic predictors alone may generally yield models whose accuracy does not differ substantially from those that also include biotic predictors, the effects of biotic interactions on the distribution of invasive plants in forests may be detectable at larger scales than previously thought.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Pushing Cu uphill of the volcano curve: Impact of a WC support on the catalytic activity of copper toward the hydrogen evolution reaction

Here, the adsorption of atomic H and H 2 on copper mono- and submonolayers supported on hexagonal WC(0001) surfaces has been investigated using density functional theory with the Perdew–Burke–Ernzerhof exchange correlation functional and D2 van der Waals corrections. Results evidence the impact of the termination of the carbide substrate on fundamental properties of Cu adatoms, and, hence, on the stability of molecular and atomic hydrogen, defining copper's catalytic activity for hydrogen evolution reaction. Using H adsorption energy as a descriptor, catalytic activity of Cu adlayers for hydrogen evolution reaction was estimated using traditional volcano curves and a curve, obtained at low hydrogen coverage. Obtained results evidence that copper adlayers supported on the WC may present a viable low-cost alternative to noble metal-based catalysts, with improved catalytic activity compared to that of copper. This, potentially, can be a useful basis for designing and developing novel functional materials with predetermined catalytic properties.

08 HYDROGEN↗
DOE OSTI2020

Carbidic Mo is the sole kinetically-relevant active site for catalytic methane dehydroaromatization on Mo/H-ZSM-5

Systematic variation of Mo/Al = {0.10, 0.25, 0.35} in Mo/H-ZSM-5 catalysts effects ~20× change of Mo-to-H + ratio during methane dehydroaromatization (DHA) reactions at steady state and on deactivating catalysts. Mo and Brønsted-acid site count, respectively enumerated by ethane hydrogenolysis probe reaction and dimethyl ether chemical titration, evolve disparately during DHA catalysis, indicating the two sites reduce in number by distinct mechanisms. Here, the deactivation of Mo/H-ZSM-5 catalytic activity is uniquely attributed to the loss of active, or accessible, Mo sites, evinced by (i) the non-selective nature of deactivation (i.e. that deactivation occurs without modification of residual active sites), (ii) invariance in product distribution from Mo/H + = 0.10–2.1, and (iii) linear correspondence between Mo-catalyzed ethane hydrogenolysis rate and active site count during DHA on deactivating samples. DHA forward rate – the intrinsic kinetic descriptor of catalyst activity – normalizes by Mo content at steady state and on deactivating catalysts with Mo/Al = 0.10–0.35 for Mo-based contact times in the range τ Mo = 0.79–44 mol Mo s mol –1 C , unequivocally establishing Mo aggregates as the sole kinetically-relevant active site on Mo/H-ZSM-5. Brønsted-acid site content over a ~40× range in τ H+ = 1.4–54 mol H+ s mol –1 C has no discernable correlation with or effect on DHA rate, methane conversion, or product distribution – demonstrating H + does not catalyze any rate- or selectivity-determining steps in the benzene formation pathway and suggesting that the benefits of zeolitic acid sites are only to disperse Mo during catalyst synthesis and, conceivably, to catalyze equilibrated reaction steps.

02 PETROLEUM↗
DOE OSTI2024

Trace Fe activates perovskite nickelate OER catalysts in alkaline media via redox-active surface Ni species formed during electrocatalysis

The accurate description of activity trends among perovskite-oxide oxygen-evolution-reaction (OER) catalysts using electronic-structure descriptors requires that the bulk structure of the catalyst is comparable to that of the surface. Few studies have addressed the dynamic nature of the catalyst's structure during the OER and the consequential implications for understanding activity. Here, we use a combination of electrochemical and materials-characterization techniques to study the surface reconstruction and the associated formation of a new redox-active phase on LaNiO 3 particles, LaNiO 3 epitaxial films, and an analogous Ruddlesden-Popper phase, La 2 NiO 4 . Small, but characteristic, redox features corresponding to Ni redox in nominally amorphous NiO x H y are observed during cyclic voltammetry of these initially fully crystalline materials. The size of these redox features grows with prolonged cycling and contributes to an increased surface area as determined from electrochemical impedance spectroscopy (EIS). Here we find the OER activity is strongly dependent on soluble Fe species in the electrolyte, common impurities in alkaline media. These observations are consistent with the reconstruction of the crystalline surface to form NiO x H y species and subsequent activation by adsorption of Fe forming the well-known and extremely active NiFeO x H y OER catalyst.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Polynomial chaos expansions on principal geodesic Grassmannian submanifolds for surrogate modeling and uncertainty quantification

In this work we introduce a manifold learning-based surrogate modeling framework for uncertainty quantification in high-dimensional stochastic systems. Our first goal is to perform data mining on the available simulation data to identify a set of low-dimensional (latent) descriptors that efficiently parameterize the response of the high-dimensional computational model. To this end, we employ Principal Geodesic Analysis on the Grassmann manifold of the response to identify a set of disjoint principal geodesic submanifolds, of possibly different dimension, that captures the variation in the data. Since operations on the Grassmann require the data to be concentrated, we propose an adaptive algorithm based on Riemannian K-means and the minimization of the sample Fréchet variance on the Grassmann manifold to identify “local” principal geodesic submanifolds that represent different system behavior across the parameter space. Polynomial chaos expansion is then used to construct a mapping between the random input parameters and the projection of the response on these local principal geodesic submanifolds. Here, the method is demonstrated on four test cases, a toy-example that involves points on a hypersphere, a Lotka-Volterra dynamical system, a continuous-flow stirred-tank chemical reactor system, and a two-dimensional Rayleigh-Bénard convection problem.

42 ENGINEERING↗
DOE OSTI2021

Dual-atom active sites embedded in two-dimensional C 2 N for efficient CO 2 electroreduction: A computational study

Double-atom catalysts (DACs) have emerged as an enhanced platform of single-atom catalyst for promoting electrocatalytic CO 2 reduction reaction (CO 2 RR). Herein, we present a density-functional theory study on CO 2 RR performance of seven C 2 N-supported homo- and heteronuclear DACs, denoted as M 2 @C 2 N. Our results demonstrate that there exists substantial synergistic effect of dual-metal-atom N 2 M 2 N 2 active site and C 2 N matrix on O&#x2550 C &#x2550O bond activation. The dual-atom M 2 sites are able to drive CO 2 RR beyond C 1 products with low limiting potential (U L ). Specifically, C 2 H 4 formation is preferred on FeM@C 2 N (M = Fe, Co, Ni, Cu) versus CH 4 formation on CuM@C 2 N (M = Co, Ni, Cu). Furthermore, *CO+*CO co-binding strength can serve as a descriptor for CO 2 RR activity for making C 2 products such that the moderate binding results in the lowest U L . Remarkably, C-affinity matters most to C—C bond coupling and C 2 H 4 formation while both C- and O-affinity control CH 4 formation. Furthermore, our results provide theoretical insight into rational design of DACs for efficient CO 2 RR.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Synergistic double-atom catalysts of metal-boron anchored on g-C 2 N for electrochemical nitrogen reduction: Mechanistic insight and catalyst screening

The rational design of a novel catalytic center with a sound basis remains both challenging and rewarding for the electrochemical reduction of N 2 (eNRR), which has provided a feasible route for achieving clean and sustainable NH 3 production under ambient conditions. Herein, using density functional theory calculations, we demonstrate that hybrid metal (M)-boron (B) double-atom catalysts (DACs) embedded in g-C 2 N substrate (M-B@C 2 N, M = 3d, 4d and 5d transition metals) can achieve both high catalytic activity and high selectivity in eNRR. The proposed M-B@C 2 N DACs have exhibited impressive feasibility and stability thanks to the resilient and robust C 2 N substrate with abundant pyridinic N atoms distributed among right-sized pore structures. Our results reveal that like the metal center, the embedded B atom can actively involve in Ntriple bondN bond activation via π*-backdonation mechanism concomitant with the substantial charge transfer to adsorbed *N 2 , leading to sizable Nsingle bondN bond elongation. Accordingly, both adsorption energy and Nsingle bondN bond length of *N 2 can be employed as catalytic descriptors for predicting eNRR activity in terms of the limiting potentials (UL). Using high-throughput screening method, we found that six M-B@C 2 N candidates have stood out as the outstanding electrocatalysts for driving eNRR, namely, M = Ti (U L = 0 V), Mo (U L = 0 V), Nb (U L = -0.04 V), W (U L = -0.23 V), Zr (U L = -0.26 V), V (U L = -0.28 V). The underlying origin is attributed to the balanced and constrained N-affinity of M-B dual site working in synergy, which can thus be used as one important guide of catalyst design.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Crystal mechanics-based thermo-elastic constitutive modeling of orthorhombic uranium using generalized spherical harmonics and first-order bounding theories

In earlier works, a mathematical procedure for invertible microstructure-property linkages was developed using computationally efficient spectral methods for polycrystalline cubic and hexagonal metals. This paper formulates such invertible microstructure–property linkages for orthorhombic polycrystalline metals relying on the generalized spherical harmonics (GSH) spectral basis. The procedure is used to compute property closures of orthorhombic polycrystals. The closures represent the complete set of theoretically possible combinations of effective properties for a selected material. The procedure relies on the first-order bounding theories and considers orientation distribution functions (ODFs) as the main microstructural descriptor influencing homogenized properties. Numerous examples of these closures involving second-rank thermal expansion and fourth-rank elastic stiffness tensorial properties over a broad range of temperatures are presented for α-uranium (α-U). In doing so, certain key properties of these closures are exploited to facilitate their computation with drastically reduced computational effort. Along with the recently developed GSH-based interpolation procedure for ODFs from coarsely spaced experimental measurement grids to finely spaced finite element mesh resolution grids presented in Barrett et al., the developed computationally efficient ODF-effective property linkages are used to establish a crystal mechanics-based simulation framework coupled with the finite element method (FEM). The ODF dependent thermal expansion and elastic stiffness tensors are efficiently calculated at every integration point and used by the FEM to predict the overall distortion of a hemispherical part made of α-U during heating. In conclusion, it is shown that the developed framework can be used to simulate microstructurally heterogeneous components under thermo-mechanical loadings in a computationally efficient manner.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A machine learning approach to quantify degradation of nuclear fuels and the effects of fission products

Nuclear fuel performance is critically dependent on understanding the evolution of fuel properties under operational conditions, a complex challenge driven by chemical changes and substantial radiation damage during fission. Traditionally, property evolution has been determined via empirical data collected following irradiation. However, these empirical correlations are limited in their applicability beyond the specific conditions in which they were obtained. This study explores a novel approach to address this challenge by applying materials informatics to develop a machine learning random forest (ML-RF) model that captures the effects of fission products on fuel compounds. The model predicts formation enthalpy (ΔH f ) by leveraging extensive quantum materials property data and correlating it with material descriptors such as composition, atomic and site features, and crystal lattice properties. This ML-RF model enables rapid interpolation across the compositional and structural spaces covered by the training data, thus supporting high-throughput screening and energetic ranking of candidate phases. The model demonstrates the ability to predict ΔH f with a mean absolute error (MAE) of approximately 0.1 to 0.2 eV/atom across a wide range of compounds, including key nuclear fuel systems (U-O, U-N, U-C, U-Si, and U-Mo). For example, it was used to assess shifts in stoichiometry for UO 2 (O/M) and UN (N/M) fuels, revealing their distinct tendencies in chemical potential variation and enabling preliminary convex hull analyses. Furthermore, the model provides insights into how individual fission products affect fuel properties. Results indicate that larger fission products (e.g., Nd, Pu, Ce) have a more pronounced impact on UO 2 , while lighter ones (e.g., Zr) strongly influence UN. Here, the model developed in this work can be used to support the Accelerated Fuel Qualification approach by facilitating preliminary evaluations prior to extensive materials modeling and experimentation. To this end, the trained model has been made available to the fuel community to support ongoing fuel development efforts.

Accelerated fuel qualification↗
DOE OSTI2023

Degradation and recovery of solid oxide fuel cell performance by control of cathode surface acidity: Case study – Impact of Cr followed by Ca infiltration

Solid oxide fuel cells (SOFC) have attracted attention as clean and efficient energy conversion devices with low emissions. However, several degradation mechanisms limit the electrochemical performance of current SOFCs, with cathode degradation due to Cr-poisoning from metal interconnects particularly problematic. The acidity/basicity of binary additives has been found to be a sensitive descriptor of the oxygen exchange kinetics, indicating that acidic Cr-species/basic Ca-species can be expected to deactivate/activate the cathode surface, respectively. Inspired by recent advances, the feasibility of relative acidity as a tool for reviving degraded SOFCs is demonstrated by neutralizing Cr-poisoned SOFCs by subsequent serial infiltration of Ca-species. Furthermore, a model mixed ionic and electronic conducting oxide, Pr 0.1 Ce 0.9 O 2-δ (PCO), is selected as the cathode material. Area-specific resistances (ASR) of symmetric cells obtained by electrochemical impedance spectroscopy show that Cr-infiltration results in a seven-fold increase in ASR, while subsequent infiltration of Ca-species leads to complete recovery. Performance degradation and recovery are attributed to depressed/enhanced redox properties at the PCO surface, as supported by XPS analysis. Experiments using anode-supported fuel cells show a reduction in peak power density by 26% upon Cr-infiltration, reversed following Ca-infiltration, after which no degradation is observed during subsequent operation for 100 h.

(Pr,Ce)O2-δ↗
DOE OSTI2026

Influence of solvents on electrochemical stability window of ionic liquid solutions

The electrochemical stability window (ESW) defines the voltage range within which an electrolyte remains stable against oxidation and reduction, serving as a key descriptor for ionic liquid (IL)-based systems. The ESWs of 15 ILs with diverse cation-anion combinations were systematically investigated, both in their neat forms and as binary mixtures with three representative solvents, i.e., propylene carbonate (PC), ethylene glycol (EG), and tetrahydrofuran (THF). Cyclic voltammetry on a glassy carbon electrode determined anodic and cathodic stability limits, revealing that ESW protection in IL-solvent mixtures strongly depends on solvent polarity, proticity, and IL compatibility. PC, with a high dielectric constant and aprotic character, broadened anodic limits and moderately stabilized cathodic limits. THF maintained compact interfacial structure, offering additional cathodic protection, whereas EG, being protic, reduced ESWs via proton-coupled electron transfer and hydrogen evolution. The solvent protection trend followed by PC > THF > EG, with bulky, hydrophobic cations ([HMIM] + , [BMPYRR] + ) and weakly coordinating anions ([NTF 2 ] - , [DCA] - , [DEP] - ) showing the highest ESW protection for 50 mol% IL in these solvents. Among the neat ILs, [EMIM][DEP], [HMIM][PF 6 ], and [BMPYRR][DCA] showed wide ESWs of ∼5.5 V. These results provide valuable insight into solvent-modulated electrochemical stability, enabling rational design of IL-based electrolytes for energy storage and electrosynthesis.

25 ENERGY STORAGE↗
DOE OSTI2021

Operando leaching of pre-incorporated Al and mechanism in transition-metal hybrids on carbon substrates for enhanced charge storage

Insufficient exposure and utilization of active sites often induces an inferior reactivity for transition-metal-based two-dimensional (2D) materials. In response, we for the first time propose a universal "nano-tailoring" strategy to incorporate abundant defects and active sites into low-crystallinity nanosheets by electrochemically leaching of Al species. With MnAl layered double hydroxides (LDHs) as a representative example, potassium-birnessite MnO 2 (AK-MnO 2 ) with oxygen vacancies and abundant edge sites is successfully produced. The oxygen vacancies are shown to help optimize the electron-transfer and ion-adsorption capability. These integrated advantages endow the AK-MnO 2 with a high capacitance value of 239 F g –1 at 100 A g –1 . By further combining with soft X-ray absorption spectroscopy techniques, we unravel that the reducibility of M 2+ in M 2+ Al-LDH serves as the key descriptor for the reconstruction rate. Here, this "nano-tailoring" strategy can provide some important implications and clues to manipulating 2D materials for efficient energy storage and conversion.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Digital polycrystalline microstructure generation using diffusion probabilistic models

Accurate micromechanical simulation of polycrystalline materials requires a realistic digital representation of the grain scale microstructure. Here, this work demonstrates the use of a generative diffusion probabilistic model for synthesizing single phase polycrystalline realizations. The model performs well and is capable of producing realistic microstructures consisting of not just simple equiaxed structures but also structures exhibiting more complex spatial arrangements. Masked microstructure generation reveals that the model is context aware of morphological descriptors which may be encoded in the latent space. Training on more diverse data sets, with scaled up architectures, may enable development of future models capable of synthesizing even more complex microstructural features.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Chemometric modeling of power conversion efficiency of organic dyes in dye sensitized solar cells for the future renewable energy

The present study reports chemometric modeling of power conversion efficiency (PCE) of dye sensitized solar cells (DSSCs) using the biggest available data set till date which comprises around 1200 dyes covering 7 chemical classes. To extract the best structural features required for higher PCE, we have developed multiple partial least squares (PLS) quantitative structure-property relationship (QSPR) models for the Triphenylamine, Phenothiazine, Indoline, Porphyrin, Coumarin, Carbazole and Diphenylamine chemical classes using descriptors derived from the best subset selection method followed by selection of best five models in each dataset based on the Mean Absolute Error (MAE) values. The models were validated both internally and externally followed by the consensus predictions employing “Intelligent Consensus Predictor” tool to examine whether the quality of predictions can be improved with the “intelligent” selection of multiple PLS models. The quality of predictions for the respective external sets showed that the consensus models (CM) are better than the individual models (IM) in most of the cases. From the insights of the developed models, we concluded that attributes like a packed structure toward higher conductivity of electrons, auxiliary donor fragment of aromatic tertiary amines, number of thiophenes inducing the bathochromic shift and augmenting the absorption, presence of additional electron donors, enhancement of electron-donating abilities, number of non-aromatic conjugated C(sp 2 ) which helps as conjugation extension units to broaden the absorption and highly conjugated $π$-systems exert positive contributions to the PCE. On the contrary, features negatively contributing to PCE are the followings: fragments which lower the tendency of localized $π–π*$ transition, fragments related to larger volume and surface area of dyes along with hydrophobicity resulting in poor adhesion, fragment RC = N causing dye hydrolysis, steric hindrance for π electronic mobility, fragments enhancing polarity, etc. Finally, the identified features from the best QSPR model of the coumarin dataset was employed in designing of ten more efficient coumarin dyes (predicted %PCE ranging from 8.93 to 10.62) than the existing ones.

14 SOLAR ENERGY↗