A5024700821- Organic Electronics and Photovoltaics
- Conducting polymers and applications
- Molecular Junctions and Nanostructures
- Perovskite Materials and Applications
- Machine Learning in Materials Science
- Italian Fascism and Post-war Society
- Quantum Dots Synthesis And Properties
- TiO2 Photocatalysis and Solar Cells
- Quantum and electron transport phenomena
- Chalcogenide Semiconductor Thin Films
- Advanced Memory and Neural Computing
- Thin-Film Transistor Technologies
- Historical and Environmental Studies
- Advanced Thermodynamics and Statistical Mechanics
- Advanced Photocatalysis Techniques
- Force Microscopy Techniques and Applications
- solar cell performance optimization
- Silicon and Solar Cell Technologies
- Electrocatalysts for Energy Conversion
- Electrochemical Analysis and Applications
- Semiconductor materials and devices
- Gas Sensing Nanomaterials and Sensors
- Fuel Cells and Related Materials
- Solid-state spectroscopy and crystallography
- Organic Light-Emitting Diodes Research
Technical University of Munich
2016-2025
University of L'Aquila
2022-2023
Nanosystems Initiative Munich
2018
University of Rome Tor Vergata
2007-2015
University of Bremen
2006-2013
Paderborn University
2005-2007
The University of Sydney
2007
Instituto de Física Teórica
2006
Indoor light harvesters enable machine learning on fully autonomous IoT devices at 2.72 × 10<sup>15</sup> photons per inference.
Tuning the energy levels of halide perovskite by controlling deposition dipolar self-assembled monolayers.
IoT devices powered by copper electrolyte-based dye-sensitized photovoltaic cells as ambient light harvesters achieve 38% power conversion efficiency and incorporate a dynamic intelligent on-device energy management system.
High oxygen reduction (ORR) activity has been for many years considered as the key to energy applications. Herein, by combining theory and experiment we prepare Pt nanoparticles with optimal size efficient ORR in proton-exchange-membrane fuel cells. Optimal nanoparticle sizes are predicted near 1, 2, 3 nm computational screening. To corroborate our results, have addressed challenge of approximately 1 sized synthesis a metal-organic framework (MOF) template approach. The electrocatalyst was...
With the demand for renewable energy and efficient devices rapidly increasing, a need arises to find optimize novel (nano)materials. sheer limitless possibilities material combinations synthetic procedures, obtaining novel, highly functional materials has been tedious trial error process. Recently, machine learning emerged as powerful tool help syntheses; however, most approaches require substantial amount of input data, limiting their pertinence. Here, three well-known machine-learning...
The commercial development of perovskite solar cells (PSCs) has been significantly delayed by the constraint performing time-consuming degradation studies under real outdoor conditions. These are necessary steps to determine device lifetime, an area where PSCs traditionally suffer. In this work, we demonstrate that behavior can be predicted employing accelerated indoor stability analyses. prediction was possible using a swift and accurate pipeline machine learning algorithms mathematical...
Abstract Metal-organic frameworks (MOF) are an attractive class of porous materials due to their immense design space, allowing for application-tailored properties. Properties interest, such as gas sorption, can be predicted in silico with molecular mechanics simulations. However, the accuracy is limited by available empirical force field and partial charge estimation scheme. In this work, we train a graph neural network prediction via active learning based on Dropout Monte Carlo. We show...
We study heating and heat dissipation of a single C(60) molecule in the junction scanning tunneling microscope by measuring electron current required to thermally decompose fullerene cage. The power for decomposition varies with energy reflects molecular resonance structure. When tip contacts can sustain much larger currents. Transport simulations explain these effects due resonant electron-phonon coupling cooling vibrational decay into upon contact formation.
Abstract Compositional engineering of perovskites has enabled the precise control material properties required for their envisioned applications in photovoltaics. However, challenges remain to address efficiency, stability, and toxicity simultaneously. Mixed lead‐free inorganic have recently demonstrated potential resolving such issues but composition space is gigantic, making it difficult discover promising candidates even using high‐throughput methods. A machine learning approach employing...
We investigate the influence of molecular vibrations on tunneling electrons through an octane−thiolate sandwiched between two gold contacts. The coherent and incoherent currents are computed using non-equilibrium Green's functions formalism. Both system Hamiltonian electron−phonon interaction obtained from first-principles DFT calculations, including a microscopic treatment This method allows to study explicitly each individual vibrational mode show detailed analysis power dissipated in wire.
We present results for a simulated inelastic electron-tunneling spectra (IETS) from calculations using the “gDFTB” code. The geometric and electronic structure is obtained local-basis density-functional scheme, nonequilibrium Green’s function formalism employed to deal with transport aspects of problem. calculated spectrum octanedithiol on gold(111) shows good agreement experimental suggests further details in assignment such spectra. show that some low-energy peaks, unassigned spectrum,...
An a priori computational method for determining intensities in inelastic electron tunneling spectroscopy (IETS) is developed that allows simple, chemically intuitive propensity rules to be obtained arbitrary applications. The molecule shown scatter charges between quite specific eigenchannels of lead-coupling-weighted molecular density states. This mode-specific scattering sites identified within the molecule, indicating how external chemical or other perturbations could used control IETS...
Perovskite-based solar cells are emerging as a potential new leading photovoltaic technology. However, several fundamental aspects of the stability remain unclear. In this Letter, we combine experimental measurements and numerical simulations to show that mesoporous interface between perovskite electron collection layer mitigates reversible performance loss associated with ion migration. We argue larger interfacial area dilutes concentration defects accumulate result migration within under...
Tailored Pt nanoparticle catalysts are promising candidates to accelerate the oxygen reduction reaction (ORR) in fuel cells. However, search for active is hindered by laborious effort of experimental synthesis and measurements. On other hand, DFT-based approaches still time consuming often not efficient. In this study, we introduce a computational model which enables rapid catalytic activity calculation unstrained pure electrocatalysts. The generic setup based on DFT results data obtained...
Abstract Insight into the relation between morphology and transport properties of organic semiconductors can be gained using multiscale simulations. Since computing electronic properties, such as intermolecular transfer integral, quantum chemical (QC) methods requires a high computational cost, existing models assume several approximations. A machine learning (ML)–based approach is presented that allows to simulate charge in considering static disorder within disordered crystals. By mapping...
First-principles simulations reveal the competition of defect formation and healing at grain boundaries in lead-halide perovskites. Fast halide migration GBs mediates structural healing, but also gives rise to enhanced Frenkel formation.
ABSTRACT Material layers at electrode/semiconductor interfaces are fundamental for the photovoltaic properties of polymer solar cells. The relationship between open‐circuit voltage ( V OC ) and work function φ these interface is still a matter debate. Simulations, together with experiments on over more than 20 cell architectures based P3HT:PC 60 BM, enabled us to analyze physical dependence . In particular, when contacts well inside gap we observe that performance depends strongly even small...
In this paper, we present our generalized kinetic Monte Carlo (kMC) framework for the simulation of organic semiconductors and electronic devices such as solar cells (OSCs) light-emitting diodes (OLEDs). Our model generalizes geometrical representation multifaceted properties material by use a non-cubic, Voronoi tessellation that connects sites to polymer chains. Herewith, obtain realistic both amorphous crystalline domains small molecules polymers. Furthermore, generalize excitonic...
Core-shell nanocatalyst activities are chiefly controlled by bimetallic material composition, shell thickness, and nanoparticle size. We present a machine learning framework predicting strain with site-specific precision to rationalize how on Pt core-shell nanocatalysts can enhance oxygen reduction activities. Large compressive Pt@Cu Pt@Ni induces optimal mass at 1.9 nm It is predicted that Pt@Au Pt@Ag have the best 2.8 nm, where active sites exposed weak strain. demonstrate depends size;...
Gaining insight into structure–property relations is a key factor for the development of organic electronics. We present multiscale framework charge carrier mobilities in thin films empowered by machine-learned transfer integrals. The choice molecular representation crucial accurate and sensitive predictions. Using pentacene films, we investigate kernel based algorithms systematically compare representations ranging from system-specific geometric to Coulomb matrix features predict absolute...
Adsorption study of environmentally toxic small gas molecules on two-dimensional (2D) materials plays a significant role in analyzing the performance sensors. In this work, density functional theory (DFT) and machine learning (ML) techniques have been employed to systematically adsorption properties CO, CO2, CH4 pristine defective planar magnesium monolayer, known as magnesene (2D-Mg). The DFT analysis showed that mechanically robust 2D-Mg retains its metallicity presence both mono...