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@article{nokey,

title = {Comparative convolutional neural networks for perovskite solar cell PCE predictions},

author = {M Harth and D K Kumar and S Kassou and K El Idrissi and R K Gupta and Y Daniel and O Makdasi and I Visoly-Fisher and A Gagliardi},

url = {\<Go to ISI\>://WOS:001544453700001},

doi = {10.1038/s41524-025-01744-w},

year  = {2025},

date = {2025-08-04},

journal = {Npj Computational Materials},

volume = {11},

number = {1},

abstract = {Imaging offers a fast and accessible means for spatial characterization of halide perovskite photovoltaic materials, yet extracting optoelectrical properties-such as power conversion efficiency (PCE)-remains challenging. This study presents a deep learning methodology that correlates optical reflective images of perovskite solar cells with their PCE by focusing on image differences rather than absolute visual features. The approach predicts relative changes in PCE by comparing images of the same device in different states (e.g., before and after encapsulation) or against a reference image. This comparative technique significantly outperforms traditional methods that attempt to directly infer PCE from a single image. Furthermore, it demonstrates high effectiveness in low-data regimes, using only 115 samples. By leveraging convolutional neural networks (CNNs) trained on small datasets, the method offers an adaptable and scalable solution for device characterization. Overall, the comparative approach enhances the accuracy and applicability of machine vision in perovskite solar cell analysis.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {AUGUR, A flexible and efficient optimization algorithm for identification of optimal adsorption sites},

author = {I Kouroudis and N Misciaci and F Mayr and L M\"{u}ller and Z Gu and A Gagliardi},

doi = {10.1038/s41524-025-01630-5},

year  = {2025},

date = {2025-05-17},

urldate = {2024-09-24},

journal = {NPJ COMPUTATIONAL MATERIALS},

volume = {11},

issue = {1},

abstract = {In this paper, we propose a novel flexible optimization pipeline for determining the optimal adsorption sites, named AUGUR (Aware of Uncertainty Graph Unit Regression). Our model combines graph neural networks and Gaussian processes to create a flexible, efficient, symmetry-aware, translation, and rotation-invariant predictor with inbuilt uncertainty quantification. This predictor is then used as a surrogate for a data-efficient Bayesian Optimization scheme to determine the optimal adsorption positions. This pipeline determines the optimal position of large and complicated clusters with far fewer iterations than current state-of-the-art approaches. Further, it does not rely on hand-crafted features and can be seamlessly employed on any molecule without any alterations. Additionally, the pooling properties of graphs allow for the processing of molecules of different sizes by the same model. This allows the energy prediction of computationally demanding systems by a model trained on comparatively smaller and less expensive ones.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Ion-Transport Kinetics and Interface Stability Augmentation of Zinc Anodes Based on Fluorinated Covalent Organic Framework Thin Films},

author = {D Lei and W Shang and L Cheng and Poonam and W Kaiser and P Banerjee and S Tu and O Henrotte and J Zhang and A Gagliardi and J Jinschek and E Cort\'{e}s and P M\"{u}ller-Buschbaum and A S Bandarenka and M Z Hussain and R A Fischer},

url = {https://doi.org/10.1002/aenm.202403030},

doi = {https://doi.org/10.1002/aenm.202403030},

issn = {1614-6832},

year  = {2024},

date = {2024-12-01},

journal = {Advanced Energy Materials},

volume = {14},

number = {46},

pages = {2403030},

abstract = {Abstract Zinc (Zn) emerges as an ideal anode for aqueous-based energy storage devices because of its safety, non-toxicity, and cost-effectiveness. However, the reversibility of zinc anodes is constrained by unchecked dendrite proliferation and parasitic side reactions. To minimize these adverse effects, a highly oriented, crystalline 2D porous fluorinated covalent organic framework (denoted as TpBD-2F) thin film is in situ synthesized on the Zn anode as a protective layer. The zincophilic and hydrophobic TpBD-2F provides numerous 1D fluorinated nanochannels, which facilitate the hopping/transfer of Zn2+ and repel H2O infiltration, thus regulating Zn2+ flux and inhibiting interfacial corrosion. The resulting TpBD-2F protective film enabled stable plating/stripping in symmetric cells for over 1200 h at 2 mA cm?2. Furthermore, assembled full cells (Zn-ion capacitors) deliver an ultra-long cycling life of over 100 000 cycles at a current density of 5 A g?1, outperforming nearly all reported porous crystalline materials.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Active learning graph neural networks for partial charge prediction of metal-organic frameworks via dropout Monte Carlo},

author = {S Thaler and F Mayr and S Thomas and A Gagliardi and J Zavadlav},

url = {https://doi.org/10.1038/s41524-024-01277-8},

doi = {10.1038/s41524-024-01277-8},

issn = {2057-3960},

year  = {2024},

date = {2024-05-03},

journal = {npj Computational Materials},

volume = {10},

number = {1},

pages = {86},

abstract = {Metal-organic frameworks (MOF) are an attractive class of porous materials due to their immense design space, allowing for application-tailored properties. Properties of interest, such as gas sorption, can be predicted in silico with molecular mechanics simulations. However, the accuracy is limited by the available empirical force field and partial charge estimation scheme. In this work, we train a graph neural network for partial charge prediction via active learning based on Dropout Monte Carlo. We show that active learning significantly reduces the required amount of labeled MOFs to reach a target accuracy. The obtained model generalizes well to different distributions of MOFs and Zeolites. In addition, the uncertainty predictions of Dropout Monte Carlo enable reliable estimation of the mean absolute error for unseen MOFs. This work paves the way towards accurate molecular modeling of MOFs via next-generation potentials with machine learning predicted partial charges, supporting in-silico material design.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {PPy-PdO modified MXene for flexible binder-free electrodes for asymmetric supercapacitors: Insights from experimental and DFT investigations},

author = {J Vigneshwaran and J Jose and S Thomas and A Gagliardi and R L Narayan and S P Jose},

url = {https://www.sciencedirect.com/science/article/pii/S1385894724020424},

doi = {https://doi.org/10.1016/j.cej.2024.150555},

issn = {1385-8947},

year  = {2024},

date = {2024-05-01},

journal = {Chemical Engineering Journal},

volume = {487},

pages = {150555},

abstract = {Binder-free, flexible electrodes of V2C MXene, V2C-PPy, and V2C-PPy-PdO (a ternary composite of vanadium carbide, polypyrrole, and palladium oxide) were fabricated using a simplified, one-step electrodeposition method. A comprehensive assessment has subsequently been conducted on the microstructural and electrochemical attributes of these electrode materials when utilized in supercapacitors with a 1 M H2SO4 electrolyte. Notably, an impressive specific capacitance of 487F g−1 is achieved for V2C-PPy-PdO ternary composite at 1 A/g. This exceptional performance is due to the considerable active surface area and inherent structural stability of the host material. These factors significantly enhanced the electrochemical reaction kinetics and cyclic reversibility. Furthermore, the V2C-PPy-PdO composite demonstrated a notable specific capacitance of 250F g−1 when integrated into an asymmetric coin cell configuration alongside activated porous carbon under a current density of 1 A/g. Remarkably, it maintained an outstanding capacitance retention of 92 % across 10,000 charge\textendashdischarge cycles. Our experimental discoveries were additionally substantiated through the Density Functional Theory calculations, which unveiled that the inclusion of PdO within the V2C-PPy-PdO composite led to an augmentation of electronic states near the Fermi level. This increase in electronic states ultimately improved the quantum capacitance, rendering the V2C-PPy-PdO composite a highly promising candidate for supercapacitor applications.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Artificial Intelligence-Based, Wavelet-Aided Prediction of Long-Term Outdoor Performance of Perovskite Solar Cells},

author = {I Kouroudis and K T Tanko and M Karimipour and A B Ali and D K Kumar and V Sudhakar and R K Gupta and I Visoly-Fisher and M Lira-Cantu and A Gagliardi},

url = {https://doi.org/10.1021/acsenergylett.4c00328},

doi = {10.1021/acsenergylett.4c00328},

year  = {2024},

date = {2024-03-19},

journal = {ACS Energy Letters},

volume = {9},

number = {4},

pages = {1581-1586},

abstract = {The commercial development of perovskite solar cells (PSCs) has been significantly delayed by the constraint of performing time-consuming degradation studies under real outdoor conditions. These are necessary steps to determine the device lifetime, an area where PSCs traditionally suffer. In this work, we demonstrate that the outdoor degradation behavior of PSCs can be predicted by employing accelerated indoor stability analyses. The prediction was possible using a swift and accurate pipeline of machine learning algorithms and mathematical decompositions. By training the algorithms with different indoor stability data sets, we can determine the most relevant stress factors, thereby shedding light on the outdoor degradation pathways. Our methodology is not specific to PSCs and can be extended to other PV technologies where degradation and its mechanisms are crucial elements of their widespread adoption.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Optoelectronic perovskite film characterization via machine vision},

author = {M Harth and L Vesce and I Kouroudis and M Stefanelli and A Di Carlo and A Gagliardi},

url = {https://www.sciencedirect.com/science/article/pii/S0038092X23004656},

doi = {https://doi.org/10.1016/j.solener.2023.111840},

issn = {0038-092X},

year  = {2023},

date = {2023-07-22},

journal = {Solar Energy},

volume = {262},

pages = {111840},

abstract = {We present our research for fast and reliable extraction of bandgap and absorption quality values for triple-cation perovskite thin films from sample scans. Our approach leverages machine learning methods, namely convolutional neural networks, to perform regression tasks aimed at predicting the properties of interest. To this end, thin film samples were synthesized via blade-coating and their photoluminescence and ultraviolet\textendashvisible spectra collected, along with the film thickness. We propose a method of computing a dimensionless figure of merit we called the Area Under Absorption Coefficient (AUAC), its purpose being to qualitatively evaluate the absorption quality of perovskite films for use in photovoltaic modules. This work demonstrates the usability of simple imaging techniques to analyze experimental samples while requiring only a feasibly acquirable initial amount of data. Our reported method can help speed up time consuming material optimizations by reducing lab time spent on recurrent characterization, nicely synergizes with high throughput production lines and could be adapted for quick extraction of other optoelectrical quantities.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Mechanistic Insights into ZIF-8 Encapsulation of Atom-Precise Pt(M) Carbonyl Clusters},

author = {K L Kollmannsberger and Poonam and C Cesari and R Khare and T Kratky and M Boniface and O Tomanec and J Michali\v{c}ka and E Mosconi and A Gagliardi and S G\"{u}nther and W Kaiser and T Lunkenbein and S Zacchini and J Warnan and R A Fischer},

url = {https://doi.org/10.1021/acs.chemmater.3c00807},

doi = {10.1021/acs.chemmater.3c00807},

issn = {0897-4756},

year  = {2023},

date = {2023-07-12},

journal = {Chemistry of Materials},

volume = {35},

number = {14},

pages = {5475-5486},

abstract = {Precisely designing metal nanoparticles (NPs) is the cornerstone for maximizing their efficiency in applications like catalysis or sensor technology. Metal\textendashorganic frameworks (MOFs) with their defined and tunable pore systems provide a confined space to host and stabilize small metal NPs. In this work, the MOF encapsulation of various atom-precise clusters following the bottle-around-ship approach is investigated, providing general insights into the scaffolding mechanism. Eleven carbonyl-stabilized Pt(M) (M = Co, Ni, Fe, and Sn) clusters are employed for the encapsulation in the zeolitic imidazolate framework (ZIF)-8. Infrared and UV/Vis spectroscopy, density functional theory, and ab initio molecular dynamics revealed structure\textendashencapsulation relationship guidelines. Thereby, cluster polarization, size, and composition were found to condition the scaffolding behavior. Encaging of [NBnMe3]2[Co8Pt4C2(CO)24] (Co8Pt4) is thus achieved as the first MOF-encapsulated bimetallic carbonyl cluster, Co8Pt4@ZIF-8, and is fully characterized including X-ray absorption near edge and extended X-ray absorption spectroscopy. ZIF-8 confinement not only promotes property changes, like the T-dependent magnetism, but it also further allows heat-induced ligand-stripping without altering the cluster size, enabling the synthesis of naked, heterometallic, close to atom-precise clusters.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Utilizing Data-Driven Optimization to Automate the Parametrization of Kinetic Monte Carlo Models},

author = {I Kouroudis and M G\"{o}\sswein and A Gagliardi},

url = {https://doi.org/10.1021/acs.jpca.3c02482},

doi = {10.1021/acs.jpca.3c02482},

issn = {1089-5639},

year  = {2023},

date = {2023-07-08},

journal = {The Journal of Physical Chemistry A},

volume = {127},

number = {28},

pages = {5967-5978},

abstract = {Kinetic Monte Carlo (kMC) simulations are a popular tool to investigate the dynamic behavior of stochastic systems. However, one major limitation is their relatively high computational costs. In the last three decades, significant effort has been put into developing methodologies to make kMC more efficient, resulting in an enhanced runtime efficiency. Nevertheless, kMC models remain computationally expensive. This is in particular an issue in complex systems with several unknown input parameters where often most of the simulation time is required for finding a suitable parametrization. A potential route for automating the parametrization of kinetic Monte Carlo models arises from coupling kMC with a data-driven approach. In this work, we equip kinetic Monte Carlo simulations with a feedback loop consisting of Gaussian Processes (GPs) and Bayesian optimization (BO) to enable a systematic and data-efficient input parametrization. We utilize the results from fast-converging kMC simulations to construct a database for training a cheap-to-evaluate surrogate model based on Gaussian processes. Combining the surrogate model with a system-specific acquisition function enables us to apply Bayesian optimization for the guided prediction of suitable input parameters. Thus, the amount of trial simulation runs can be considerably reduced facilitating an efficient utilization of arbitrary kMC models. We showcase the effectiveness of our methodology for a physical process of growing industrial relevance: the space-charge layer formation in solid-state electrolytes as it occurs in all-solid-state batteries. Our data-driven approach requires only 1\textendash2 iterations to reconstruct the input parameters from different baseline simulations within the training data set. Moreover, we show that the methodology is even capable of accurately extrapolating into regions outside the training data set which are computationally expensive for direct kMC simulation. Concluding, we demonstrate the high accuracy of the underlying surrogate model via a full parameter space investigation eventually making the original kMC simulation obsolete.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Nanostructured Ternary Nickel-Based Mixed Anionic (Telluro)-Selenide as a Superior Catalyst for Oxygen Evolution Reaction},

author = {I M Abdullahi and S Thomas and A Gagliardi and M A Zaeem and M Nath},

url = {https://doi.org/10.1002/ente.202300177},

doi = {https://doi.org/10.1002/ente.202300177},

issn = {2194-4288},

year  = {2023},

date = {2023-07-01},

journal = {Energy Technology},

volume = {11},

number = {7},

pages = {2300177},

abstract = {Developing protocols for designing high-efficiency, durable, cost-effective electrocatalysts for oxygen evolution reaction (OER) necessitates deeper understanding of structure?property correlation as a function of composition. Herein, it has been demonstrated that incorporating tellurium into binary nickel chalcogenide (NiSe) and creating a mixed anionic phase perturbs its electronic structure and significantly enhances the OER activity. A series of nanostructured nickel chalcogenides comprising a layer-by-layer morphology along with mixed anionic ternary phase are grown in?situ on nickel foam with varying morphological textures using simple hydrothermal synthesis route. Comprehensive X-ray diffraction, X-ray photoelectron spectroscopy, and in?situ Raman spectroscopy analysis confirms the formation of a trigonal single-phase nanocrystalline nickel (telluro)-selenide (NiSeTe) as a truly mixed anionic composition. The NiSeTe electrocatalyst exhibits excellent OER performance, with a low overpotential of 300?mV at 50?mA?cm?2 and a small Tafel slope of 98?mV?dec?1 in 1?m KOH electrolyte. The turnover frequency and mass activity are 0.047?s?1 and 90.3?Ag?1, respectively. Detailed electrochemical measurements also reveal enhanced charge transfer properties of the NiSeTe phase compared to the mixture of binaries. Density functional theory calculations reveal favorable OH adsorption energy in the mixed anionic phase compared to the binary chalcogenides confirming superior electrocatalytic property.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Mass transport and charge transfer through an electrified interface between metallic lithium and solid-state electrolytes},

author = {L Katzenmeier and M G\"{o}\sswein and L Carstensen and J Sterzinger and M Ederer and P M\"{u}ller-Buschbaum and A Gagliardi and A S Bandarenka},

url = {https://doi.org/10.1038/s42004-023-00923-4},

doi = {10.1038/s42004-023-00923-4},

issn = {2399-3669},

year  = {2023},

date = {2023-06-15},

journal = {Communications Chemistry},

volume = {6},

number = {1},

pages = {124},

abstract = {All-solid-state Li-ion batteries are one of the most promising energy storage devices for future automotive applications as high energy density metallic Li anodes can be safely used. However, introducing solid-state electrolytes needs a better understanding of the forming electrified electrode/electrolyte interface to facilitate the charge and mass transport through it and design ever-high-performance batteries. This study investigates the interface between metallic lithium and solid-state electrolytes. Using spectroscopic ellipsometry, we detected the formation of the space charge depletion layers even in the presence of metallic Li. That is counterintuitive and has been a subject of intense debate in recent years. Using impedance measurements, we obtain key parameters characterizing these layers and, with the help of kinetic Monte Carlo simulations, construct a comprehensive model of the systems to gain insights into the mass transport and the underlying mechanisms of charge accumulation, which is crucial for developing high-performance solid-state batteries.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Machine learning and DFT investigation of CO, CO2 and CH4 adsorption on pristine and defective two-dimensional magnesene},

author = {S Thomas and F Mayr and A Kulangara Madam and A Gagliardi},

url = {http://dx.doi.org/10.1039/D3CP00613A},

doi = {10.1039/D3CP00613A},

issn = {1463-9076},

year  = {2023},

date = {2023-04-18},

journal = {Physical Chemistry Chemical Physics},

volume = {25},

number = {18},

pages = {13170-13182},

abstract = {Adsorption study of environmentally toxic small gas molecules on two-dimensional (2D) materials plays a significant role in analyzing the performance of sensors. In this work, density functional theory (DFT) and machine learning (ML) techniques have been employed to systematically study the adsorption properties of CO, CO2, and CH4 gas molecules on the pristine and defective planar magnesium monolayer, known as magnesene (2D-Mg). The DFT analysis showed that mechanically robust 2D-Mg retains its metallicity in the presence of both mono and di-vacancy defects. Our observations have shown that 2D-Mg, whether defective or pristine, exhibits distinct adsorption behaviors towards CO, CO2, and CH4 gas molecules, including varying chemisorption and physisorption, charge transfer, and distance from the gas molecules. When analyzing the recovery time of gas molecules at room temperature, it is clear that adsorption energy has a direct correlation with the adsorption\textendashdesorption cycles, and CH4 possesses an ultra-low recovery time (15.27 ps) compared to CO2 (1.04 ns) and CO (0.90 μs) molecules. The analysis showed that defects do not have a significant impact on the work function of 2D-Mg. However, the work function decreased upon adsorption of CH4, resulting in improved sensitivity due to changes in the electronic properties. Additionally, we explored supervised ML regression models to evaluate their ability to act as a surrogate for the DFT-based adsorption energy calculation. Using both system statistics and smooth overlap of atomic position (SOAP)-based featurization, we observed that adsorption energies can be predicted with a mean absolute error of 0.10 eV.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Adsorption and sensing properties of SF6 decomposed gases on Mg-MOF-74},

author = {S Thomas and F Mayr and A Gagliardi},

url = {https://www.sciencedirect.com/science/article/pii/S0038109823000571},

doi = {https://doi.org/10.1016/j.ssc.2023.115120},

issn = {0038-1098},

year  = {2023},

date = {2023-04-01},

journal = {Solid State Communications},

volume = {363},

pages = {115120},

abstract = {Using the framework of density functional theory (DFT), this work investigated the adsorption properties of SF6 decomposed H2S, SO2, SOF2, and SO2F2 gases on a magnesium-based metal-organic framework (Mg-MOF-74). Further, the possible application of this MOF as a suitable sensor for the detection of these gas molecules is discussed through the analysis of adsorption energies, electronic properties, charge transfer, selectivity and sensitivity features and recovery time. The study revealed that the SO2 molecule possesses relatively high adsorption energy (−1.26 eV) compared to H2S (−0.10 eV), SOF2 (−0.08 eV), and SO2F2 (−0.04 eV) molecules. Results show that all the molecules are physisorbed on the semiconducting Mg-MOF-74 and the adsorption energy has a strong correlation with the electronic properties. This reflects in the bandgap and the density of states analysis shows that the adsorption of H2S, SOF2, and SO2F2 molecules leads to insignificant changes in the bandgap, whereas the adsorption of SO2 reduces the bandgap by almost 50%. The recovery time analysis also ensures that Mg-MOF-74 has a fast desorption ability for H2S, SOF2, and SO2F2 molecules, suitable for resistive and reusable chemical sensors for industrial applications.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Rapid Data-Efficient Optimization of Perovskite Nanocrystal Syntheses through Machine Learning Algorithm Fusion},

author = {C Lampe and I Kouroudis and M Harth and S Martin and A Gagliardi and A S Urban},

url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/adma.202208772},

doi = {https://doi.org/10.1002/adma.202208772},

issn = {0935-9648},

year  = {2023},

date = {2023-01-21},

urldate = {2023-01-21},

journal = {Advanced Materials},

volume = {35},

issue = {16},

pages = {2208772},

abstract = {Abstract With the demand for renewable energy and efficient devices rapidly increasing, a need arises to find and optimize novel (nano)materials. With sheer limitless possibilities for material combinations and synthetic procedures, obtaining novel, highly functional materials has been a tedious trial and error process. Recently, machine learning has emerged as a powerful tool to help optimize syntheses; however, most approaches require a substantial amount of input data, limiting their pertinence. Here, we merge three well-known machine-learning models with Bayesian Optimization into one to optimize the synthesis of CsPbBr3 nanoplatelets with limited data demand. The algorithm can accurately predict the photoluminescence emission maxima of nanoplatelet dispersions using only the three precursor ratios as input parameters. This allowed us to fabricate previously unobtainable 7 and 8 monolayer-thick nanoplatelets. Moreover, the algorithm dramatically improved the homogeneity of 2-6 monolayer-thick nanoplatelet dispersions, as evidenced by narrower and more symmetric photoluminescence spectra. Decisively, only 200 total syntheses were required to achieve this vast improvement, highlighting how rapidly material properties can be optimized. The algorithm is highly versatile and can incorporate additional synthetic parameters. Accordingly, it is readily applicable to other less-explored nanocrystal syntheses and can help rapidly identify and improve exciting compositions' quality. This article is protected by copyright. All rights reserved},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Defect formation and healing at grain boundaries in lead-halide perovskites},

author = {W Kaiser and K Hussain and A Singh and A A Alothman and D Meggiolaro and A Gagliardi and E Mosconi and F De Angelis},

url = {http://dx.doi.org/10.1039/D2TA06336K},

doi = {10.1039/D2TA06336K},

issn = {2050-7488},

year  = {2022},

date = {2022-11-11},

journal = {Journal of Materials Chemistry A},

volume = {10},

number = {46},

pages = {24854-24865},

abstract = {Polycrystalline perovskite solar cells show high power conversion efficiencies despite the presence of grain boundaries (GBs). The benign nature of GBs on the electronic properties and structural stability in metal-halide perovskites contradicts their propensity for point defect formation, a controversy that is far from being understood. In this work, we combine ab initio molecular dynamics and density functional theory calculations on the Σ5[130] GB of cesium lead iodide, CsPbI3, to shed light on the structural and electronic properties of such GBs. Our results present the first evidence of structural healing of GBs driven by the facile migration of iodine ions, resulting in stabilized GB structures with reduced hole trap states and shallow electron trap states by strain-induced Pb\textendashPb dimers. Drift-diffusion simulations reveal that shallow electron trap states in GB mainly lower open-circuit voltage by enhanced non-radiative recombination. Finally, we observe the spontaneous formation of iodine Frenkel defects with reduced formation energies compared to the perovskite bulk. Overall, our study reveals a controversy of GBs showing a moderate impact on the electronic properties by structural healing but a detrimental impact on the point defect densities, both being connected to the facile migration of iodine ions in GBs.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Machine-Learning-Optimized Perovskite Nanoplatelet Synthesis},

author = {C Lampe and I Kouroudis and M Harth and S Martin and A Gagliardi and A S Urban},

url = {https://arxiv.org/abs/2210.09783},

doi = {https://doi.org/10.48550/arXiv.2210.09783},

year  = {2022},

date = {2022-10-18},

journal = {arXiv preprint arXiv:2210.09783},

abstract = {With the demand for renewable energy and efficient devices rapidly increasing, a need arises to find and optimize novel (nano)materials. This can be an extremely tedious process, often relying significantly on trial and error. Machine learning has emerged recently as a powerful alternative; however, most approaches require a substantial amount of data points, i.e., syntheses. Here, we merge three machine-learning models with Bayesian Optimization and are able to dramatically improve the quality of CsPbBr3 nanoplatelets (NPLs) using only approximately 200 total syntheses. The algorithm can predict the resulting PL emission maxima of the NPL dispersions based on the precursor ratios, which lead to previously unobtainable 7 and 8 ML NPLs. Aided by heuristic knowledge, the algorithm should be easily applicable to other nanocrystal syntheses and significantly help to identify interesting compositions and rapidly improve their quality.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Flexible quasi-solid-state supercapacitors based on Ti3C2-Polypyrrole nanocomposites},

author = {J Vigneshwaran and J Jose and S Thomas and A Gagliardi and M Thelakkat and S P Jose},

url = {https://www.sciencedirect.com/science/article/pii/S0013468622012087},

doi = {https://doi.org/10.1016/j.electacta.2022.141051},

issn = {0013-4686},

year  = {2022},

date = {2022-10-10},

journal = {Electrochimica Acta},

volume = {429},

pages = {141051},

abstract = {Polypyrrole (PPy) based MXene nanocomposite electrode was prepared by intercalating PPy into the layered Ti3C2Tx by a harmonious electrodeposition technique. The enhanced energy storage performance of Ti3C2-PPy is studied both experimentally and by using the first-principles method. The nanotubular flower-like morphology effectively prevents Ti3C2 stacking, resulting in enhanced interlamellar spacings and for fast and precise electrons/ions pathways. Ti3C2-PPy delivers excellent specific capacitance of 474 F g−1 in 1 M H2SO4 at a current density of 1 A g−1. The fabricated asymmetric supercapacitor establishes a gravimetric capacitance of 243 F g−1 with a remarkable rate performance of 98 % across 10000 cycles. To realize better energy storage and safety standards for commercial applications, the quasi-solid-state supercapacitors were fabricated with gel polymer electrolyte. They demonstrated the key performance parameters of high energy density (54.4 Wh Kg−1) and power density (181.5 W kg−1) at an enhanced operating potential window (∼2V). This is the widest potential window reported to date for MXene-based polymeric supercapacitors. The calculated quantum capacitance value follows the experimental trend with a high value of 2104 µFcm−2 at 1.7 V. This asymmetric supercapacitor can power LEDs and establishes its potential to encounter practical applications in energy storage solutions.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Rational design of selenium inserted 1T/2H mixed-phase molybdenum disulfide for energy storage and pollutant degradation applications},

author = {K Namsheer and S Thomas and A Sharma and S A Thomas and K A Sree Raj and V Kumar and A Gagliardi and A Aravind and C S Rout},

url = {https://dx.doi.org/10.1088/1361-6528/ac80ca},

doi = {10.1088/1361-6528/ac80ca},

issn = {0957-4484},

year  = {2022},

date = {2022-08-15},

journal = {Nanotechnology},

volume = {33},

number = {44},

pages = {445703},

abstract = {MoS2 based materials are recognized as the promising candidate for multifunctional applications due to its unique physicochemical properties. But presence of lower number of active sites, poor electrical conductivity, and less stability of 2H and 1T MoS2 inherits its practical applications. Herein, we synthesized the Se inserted mixed-phase 2H/1T MoS2 nanosheets with abundant defects sites to achieve improved overall electrochemical activity. Moreover, the chalcogen insertion induces the recombination of photogenerated excitons and enhances the life of carriers. The bifunctional energy storage and photocatalytic pollutant degradation studies of the prepare materials are carried out. Fabricated symmetric solid-state supercapacitor showed an exceptional capacitance of 178 mF cm−2 with an excellent energy density of 8 μWh cm−2 and power density of 137 mW cm−2, with remarkable capacitance retention of 86.34% after successive 8000 charge\textendashdischarge cycles. The photocatalytic dye degradation experiments demonstrate that the prepared Se incorporated 1T/2H MoS2 is a promising candidate for dye degradation applications. Further, the DFT studies confirmed that the Se inserted MoS2 is a promising electrode material for supercapacitor applications with higher CQ due to a larger density of states near Fermi level as compared to pristine MoS2.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Modeling of Space-Charge Layers in Solid-State Electrolytes: A Kinetic Monte Carlo Approach and Its Validation},

author = {L Katzenmeier and M G\"{o}\sswein and A Gagliardi and A S Bandarenka},

url = {https://doi.org/10.1021/acs.jpcc.2c02481},

doi = {10.1021/acs.jpcc.2c02481},

issn = {1932-7447},

year  = {2022},

date = {2022-06-23},

journal = {The Journal of Physical Chemistry C},

volume = {126},

number = {26},

pages = {10900-10909},

abstract = {The space-charge layer (SCL) phenomenon in Li+-ion-conducting solid-state electrolytes (SSEs) is gaining much interest in different fields of solid-state ionics. Not only do SCLs influence charge-transfer resistance in all-solid-state batteries but also are analogous to their electronic counterpart in semiconductors; they could be used for Li+-ionic devices. However, the rather “elusive” nature of these layers, which occur on the nanometer scale and with only small changes in concentrations, makes them hard to fully characterize experimentally. Theoretical considerations based on either electrochemical or thermodynamic models are limited due to missing physical, chemical, and electrochemical parameters. In this work, we use kinetic Monte Carlo (kMC) simulations with a small set of input parameters to model the spatial extent of the SCLs. The predictive power of the kMC model is demonstrated by finding a critical range for each parameter in which the space-charge layer growth is significant and must be considered in electrochemical and ionic devices. The time evolution of the charge redistribution is investigated, showing that the SCLs form within 500 ms after applying a bias potential.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Local Temporal Acceleration Scheme to Couple Transport and Reaction Dynamics in Kinetic Monte Carlo Models of Electrochemical Systems},

author = {M G\"{o}\sswein and W Kaiser and A Gagliardi},

url = {https://doi.org/10.1021/acs.jctc.1c01010},

doi = {10.1021/acs.jctc.1c01010},

issn = {1549-9618},

year  = {2022},

date = {2022-05-10},

journal = {Journal of Chemical Theory and Computation},

volume = {18},

number = {5},

pages = {2749-2763},

abstract = {Kinetic Monte Carlo (kMC) simulations are a well-established tool for investigating the operation of electrochemical systems. Standard kMC algorithms become unfeasible in the presence of processes on vastly different time scales. In electrochemical systems, such time scale disparities often arise between fast transport processes and slow electrochemical reactions. A promising approach to overcome time scale disparities in kMC models is given by temporal acceleration schemes. In this work, we present a local temporal acceleration scheme to bridge the time scale disparity between fast transport and slow reaction dynamics. We combine the superbasin concept with a local, particle-based criterion for the quasi-equilibrium detection and a partitioning of transitions and particles in the system into process chains. Scaling of entire quasi-equilibrated process chains considerably reduces the computational effort without disturbing the relative dynamics of transitions within a process chain. The methodology is outlined for a hybrid organic\textendashaqueous electrolyte device which links fast electronic processes in an organic semiconductor with slow reduction reactions at its interface to the electrolyte. Our approach captures local inhomogeneities such that local physical quantities can be reproduced accurately. Additionally, we show that previous accelerated superbasin algorithms are limited by the presence of spatially varying time scale disparities. Our algorithm achieves an acceleration of several orders of magnitude providing a serious alternative to replace existing multiscale models by stand-alone kMC simulations.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Machine Learning and Optoelectronic Materials Discovery: A Growing Synergy},

author = {F Mayr and M Harth and I Kouroudis and M Rinderle and A Gagliardi},

url = {https://doi.org/10.1021/acs.jpclett.1c04223},

doi = {10.1021/acs.jpclett.1c04223},

year  = {2022},

date = {2022-03-03},

journal = {The Journal of Physical Chemistry Letters},

volume = {13},

number = {8},

pages = {1940-1951},

abstract = {Novel optoelectronic materials have the potential to revolutionize the ongoing green transition by both providing more efficient photovoltaic (PV) devices and lowering energy consumption of devices like LEDs and sensors. The lead candidate materials for these applications are both organic semiconductors and more recently perovskites. This Perspective illustrates how novel machine learning techniques can help explore these materials, from speeding up ab initio calculations toward experimental guidance. Furthermore, based on existing work, perspectives around machine-learned molecular dynamics potentials, physically informed neural networks, and generative methods are outlined.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Nonequilibrium Thermodynamics of Charge Separation in Organic Solar Cells},

author = {W Kaiser and V Jankovi\'{c} and N Vukmirovi\'{c} and A Gagliardi},

url = {https://doi.org/10.1021/acs.jpclett.1c01817},

doi = {10.1021/acs.jpclett.1c01817},

year  = {2021},

date = {2021-07-07},

journal = {The Journal of Physical Chemistry Letters},

volume = {12},

number = {27},

pages = {6389-6397},

abstract = {This work presents a novel theoretical description of the nonequilibrium thermodynamics of charge separation in organic solar cells (OSCs). Using stochastic thermodynamics, we take realistic state populations derived from the phonon-assisted dynamics of electron\textendashhole pairs within photoexcited organic bilayers to connect the kinetics with the free energy profile of charge separation. Hereby, we quantify for the first time the difference between nonequilibrium and equilibrium free energy profile. We analyze the impact of energetic disorder and delocalization on free energy, average energy, and entropy. For a high disorder, the free energy profile is well-described as equilibrated. We observe significant deviations from equilibrium for delocalized electron\textendashhole pairs at a small disorder, implying that charge separation in efficient OSCs proceeds via a cold but nonequilibrated pathway. Both a large Gibbs entropy and large initial electron\textendashhole distance provide an efficient charge separation, while a decrease in the free energy barrier does not necessarily enhance charge separation.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Global Property Prediction: A Benchmark Study on Open-Source, Perovskite-like Datasets},

author = {F Mayr and A Gagliardi},

url = {https://doi.org/10.1021/acsomega.1c00991},

doi = {10.1021/acsomega.1c00991},

year  = {2021},

date = {2021-05-03},

journal = {ACS Omega},

volume = {6},

number = {19},

pages = {12722-12732},

abstract = {Screening combinatorial space for novel materials, such as perovskite-like ones for photovoltaics, has resulted in a high amount of simulated high-throughput data and analysis thereof. This study proposes a comprehensive comparison of structural fingerprint-based machine learning models on seven open-source databases of perovskite-like materials to predict band gaps and energies. It shows that none of the given methods, including graph neural networks, are able to capture arbitrary databases evenly, while underlining that commonly used metrics are highly database-dependent in typical workflows. In addition, the applicability of variance selection and autoencoders to significantly reduce fingerprint size indicates that models built with common fingerprints only rely on a submanifold of the available fingerprint space.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Role of cation-mediated recombination in perovskite solar cells},

author = {A Singh and W Kaiser and A Gagliardi},

url = {https://www.sciencedirect.com/science/article/pii/S0927024820305092},

doi = {https://doi.org/10.1016/j.solmat.2020.110912},

issn = {0927-0248},

year  = {2021},

date = {2021-03-01},

journal = {Solar Energy Materials and Solar Cells},

volume = {221},

pages = {110912},

abstract = {The origin of the hysteresis in the current\textendashvoltage (J\textendashV) characteristics in perovskite solar cells (PSCs) is one of the most debated topics of recent years. Hysteretic effects are connected with the slow redistribution of ionic defects during the voltage sweep. Existing literature focuses on the potential screening due to accumulated ions, solely, while neglecting the possibility of charge trapping and subsequent recombination via ions. We investigate the role of cation-mediated recombination of ions using time-dependent drift\textendashdiffusion simulations in MAPbI3 PSCs. Slow-moving cations are considered as traps for the electrons. Trapped electrons can subsequently recombine non-radiatively with holes. We analyze the role of the cation-mediated trapping and its parameters (capture coefficient, cation energy, ion mobility) as well as the scan rate on the device performance. For shallow cation energies, a decrease in open-circuit voltage and slight enhancement in hysteresis is observed. Deep cation energies lead to a substantial deterioration of device performance and large hysteresis enhancement. The presented study emphasizes the importance of considering the interaction of ions with charge carriers beyond the simple electrostatic models to improve our understanding of PSCs.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Origin of Hole Transport in Small Molecule Dilute Donor Solar Cells},

author = {W Kaiser and L N S Murthy and C-L Chung and K-T Wong and J W P Hsu and A Gagliardi},

url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/aesr.202000042},

doi = {https://doi.org/10.1002/aesr.202000042},

issn = {2699-9412},

year  = {2021},

date = {2021-02-01},

journal = {Advanced Energy and Sustainability Research},

volume = {2},

number = {3},

pages = {2000042},

abstract = {Dilute donor organic solar cells (OSCs) are a promising technology to circumvent the trade-off between open-circuit voltage (Voc) and short-circuit current density (Jsc). The origin of hole transport in OSCs with donor concentrations below the percolation threshold is diversely discussed in the community. Herein, both hole back transfer and long-range hopping (tunneling) are analyzed as possible mechanisms of photocurrent in small molecule dilute donor OSCs using kinetic Monte Carlo (kMC) simulations. In contrast to previous kMC studies, the driving force for exciton dissociation is accounted for. As a study system, nitrogen-bridged terthiophene (NBTT) molecules in a [6,6]-phenyl-C70-butyric acid methyl ester (PC71BM) matrix are investigated. The simulations show that hole back transfer from the small molecule donor to the fullerene matrix explains the measured concentration dependences of the photocurrents as well as the Jsc dependence on the light intensity for donor concentrations below 5 wt%. For 5 wt%, distances between NBTT molecules decrease to reasonable ranges that long-range hopping or tunneling cannot be discounted. Compared with polymer donors, larger hole localization is observed. The results emphasize that the barrier for hole back transfer is not only due to the highest occupied molecular orbital (HOMO) offset, but also by hole localization.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Deep Learning Total Energies and Orbital Energies of Large Organic Molecules Using Hybridization of Molecular Fingerprints},

author = {O Rahaman and A Gagliardi},

url = {https://doi.org/10.1021/acs.jcim.0c00687},

doi = {10.1021/acs.jcim.0c00687},

issn = {1549-9596},

year  = {2020},

date = {2020-10-29},

journal = {Journal of Chemical Information and Modeling},

volume = {60},

number = {12},

pages = {5971-5983},

abstract = {The ability to predict material properties without the need for resource-consuming experimental efforts can immensely accelerate material and drug discovery. Although ab initio methods can be reliable and accurate in making such predictions, they are computationally too expensive on a large scale. The recent advancements in artificial intelligence and machine learning as well as the availability of large quantum mechanics derived datasets enable us to train models on these datasets as a benchmark and to make fast predictions on much larger datasets. The success of these machine learning models highly depends on the machine-readable fingerprints of the molecules that capture their chemical properties as well as topological information. In this work, we propose a common deep learning-based framework to combine different types of molecular fingerprints to enhance prediction accuracy. A graph neural network (GNN), many-body tensor representation (MBTR), and a set of simple molecular descriptors (MD) were used to predict the total energies, highest occupied molecular orbital (HOMO) energies, and lowest unoccupied molecular orbital (LUMO) energies of a dataset containing ∼62k large organic molecules with complex aromatic rings and remarkably diverse functional groups. The results demonstrate that a combination of best performing molecular fingerprints can produce better results than the individual ones. The simple and flexible deep learning framework developed in this work can be easily adapted to incorporate other types of molecular fingerprints.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Stepping Out of Equilibrium: The Quest for Understanding the Role of Non-Equilibrium (Thermo-)Dynamics in Electronic and Electrochemical Processes},

author = {W Kaiser and A Gagliardi},

url = {https://www.mdpi.com/1099-4300/22/9/1013},

issn = {1099-4300},

year  = {2020},

date = {2020-09-10},

journal = {Entropy},

volume = {22},

number = {9},

pages = {1013},

abstract = {This editorial aims to interest researchers and inspire novel research on the topic of non-equilibrium Thermodynamics and Monte Carlo for Electronic and Electrochemical Processes. We present a brief outline on recent progress and challenges in the study of non-equilibrium dynamics and thermodynamics using numerical Monte Carlo simulations. The aim of this special issue is to collect recent advances and novel techniques of Monte Carlo methods to study non-equilibrium electronic and electrochemical processes at the nanoscale.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Machine-Learned Charge Transfer Integrals for Multiscale Simulations in Organic Thin Films},

author = {M Rinderle and W Kaiser and A Mattoni and A Gagliardi},

url = {https://doi.org/10.1021/acs.jpcc.0c04355},

doi = {10.1021/acs.jpcc.0c04355},

issn = {1932-7447},

year  = {2020},

date = {2020-07-20},

journal = {The Journal of Physical Chemistry C},

volume = {124},

number = {32},

pages = {17733-17743},

abstract = {Gaining insight into structure\textendashproperty relations is a key factor for the development of organic electronics. We present a multiscale framework for charge carrier mobilities in organic thin films empowered by machine-learned charge transfer integrals. The choice of the molecular representation is crucial for accurate and sensitive predictions. Using pentacene thin films, we investigate kernel based algorithms and systematically compare representations ranging from system-specific geometric to Coulomb matrix features to predict absolute and logarithmic transfer integrals. We use the predicted transfer integrals to compute the mobility, including its anisotropy, and compare it to reference values. Best accuracies were obtained by models using the interaction part of the Coulomb matrix as a feature and the logarithm of the transfer integral as a target. We achieve R2 values of 0.97 for transfer integrals within an extensive range of 20 orders of magnitude and less than 27% error in the mobility. We show the transferability of the CIP feature for tetracene and DNTT with excellent prediction accuracies. Furthermore, we demonstrate that the interaction part of the CM successfully encodes the molecular identity and provides a highly sensitive ML framework. The presented framework opens the possibility for highly accurate mesoscopic transport simulations saving orders of magnitude in computational cost.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{,

title = {Oxygen Reduction Activities of Strained Platinum Core\textendashShell Electrocatalysts Predicted by Machine Learning},

author = {M R\"{u}ck and B Garlyyev and F Mayr and A S Bandarenka and A Gagliardi},

url = {https://doi.org/10.1021/acs.jpclett.0c00214},

doi = {10.1021/acs.jpclett.0c00214},

year  = {2020},

date = {2020-02-14},

urldate = {2020-02-14},

journal = {The Journal of Physical Chemistry Letters},

volume = {11},

pages = {1773-1780},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{,

title = {Optimizing the Size of Platinum Nanoparticles for Enhanced Mass Activity in the Electrochemical Oxygen Reduction Reaction},

author = {B Garlyyev and K Kratzl and M R\"{u}ck and J Michali\v{c}ka and J Fichtner and J M Macak and T Kratky and S G\"{u}nther and M Cokoja and A S Bandarenka and A Gagliardi and R A Fischer},

url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/anie.201904492},

doi = {10.1002/anie.201904492},

issn = {1433-7851},

year  = {2019},

date = {2019-05-03},

journal = {Angewandte Chemie International Edition},

volume = {58},

number = {28},

pages = {9596-9600},

abstract = {Abstract High oxygen reduction (ORR) activity has been for many years considered as the key to many energy applications. Herein, by combining theory and experiment we prepare Pt nanoparticles with optimal size for the efficient ORR in proton-exchange-membrane fuel cells. Optimal nanoparticle sizes are predicted near 1, 2, and 3 nm by computational screening. To corroborate our computational results, we have addressed the challenge of approximately 1 nm sized Pt nanoparticle synthesis with a metal\textendashorganic framework (MOF) template approach. The electrocatalyst was characterized by HR-TEM, XPS, and its ORR activity was measured using a rotating disk electrode setup. The observed mass activities (0.87±0.14 A mgPt−1) are close to the computational prediction (0.99 A mgPt−1). We report the highest to date mass activity among pure Pt catalysts for the ORR within similar size range. The specific and mass activities are twice as high as the Tanaka commercial Pt/C catalysis.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Oxygen Reduction Reaction: Rapid Prediction of Mass Activity of Nanostructured Platinum Electrocatalysts},

author = {M R\"{u}ck and A S Bandarenka and F Calle-Vallejo and A Gagliardi},

url = {https://doi.org/10.1021/acs.jpclett.8b01864},

doi = {10.1021/acs.jpclett.8b01864},

year  = {2018},

date = {2018-07-20},

urldate = {2018-07-20},

journal = {The Journal of Physical Chemistry Letters},

volume = {9},

number = {15},

pages = {4463-4468},

abstract = {Tailored Pt nanoparticle catalysts are promising candidates to accelerate the oxygen reduction reaction (ORR) in fuel cells. However, the search for active nanoparticle catalysts is hindered by the laborious effort of experimental synthesis and measurements. On the other hand, density functional theory-based approaches are still time-consuming and often not efficient. In this study, we introduce a computational model which enables rapid catalytic activity calculation of unstrained pure Pt nanoparticle electrocatalysts. Regarding particle size effects on Pt nanoparticles, experimental catalytic mass activities from previous studies are accurately reproduced by our computational model. Moreover, beyond available experiments, our computational model identifies potential enhancement in mass activity up to 190% over the experimentally detected maximum. Importantly, the rapid activity calculation enabled by our computational model may pave the way for extensive nanoparticle screening to expedite the search for improved electrocatalysts.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}