27 hasil untuk "Pascal Friederich"
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Artikel Jurnal
De Novo Calculation of the Charge Carrier Mobility in Amorphous Small Molecule Organic Semiconductors
Organic semiconductors (OSC) are key components in applications such as organic photovoltaics, organic sensors, transistors and organic light emitting diodes (OLED). OSC devices, especially OLEDs, often consist of multiple layers comprising one or more species of organic molecules. The unique…
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Artikel Jurnal
Concentration dependent energy levels shifts in donor-acceptor mixtures due to intermolecular electrostatic interaction
Abstract Recent progress in the improvement of organic solar cells lead to a power conversion efficiency to over 16%. One of the key factors for this improvement is a more favorable energy level alignment between donor and acceptor materials, which demonstrates that the properties of interfaces…
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Artikel Jurnal
Disorder compensation controls doping efficiency in organic semiconductors
Though conductivity doping in organic semiconductors has been widely studied in organic electronics, a clear mechanistic picture that explains the phenomenon is still lacking. Here, the authors report a theoretical approach to elucidate the role of disorder compensation in doped organic materials.
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Artikel Jurnal
Symmetry-aware Bayesian flow networks for crystal generation
Abstract The discovery of new crystalline materials is essential to scientific and technological progress. However, traditional trial-and-error approaches are inefficient due to the vast search space. Recent advancements in machine learning have enabled generative models to predict new stable…
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Artikel Jurnal
Development of aluminum oxide slurries for additive manufacturing by Bayesian optimization
Additive manufacturing by vat photopolymerization (VPP) enables the flexible production of ceramic components. The process requires ceramic slurries consisting of a photosensitive binder system and ceramic powder. To prevent defects during debinding and sintering, the highest possible content of…
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Artikel Jurnal
3DSC - a dataset of superconductors including crystal structures
Abstract Data-driven methods, in particular machine learning, can help to speed up the discovery of new materials by finding hidden patterns in existing data and using them to identify promising candidate materials. In the case of superconductors, the use of data science tools is to date slowed…
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Artikel Jurnal
Designing and understanding light-harvesting devices with machine learning
Photon-induced charge separation phenomena are at the heart of light-harvesting applications but challenging to be described by quantum mechanical models. Here the authors illustrate the potential of machine-learning approaches towards understanding the fundamental processes governing electronic…
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Artikel Jurnal
Actively learning costly reward functions for reinforcement learning
Transfer of recent advances in deep reinforcement learning to real-world applications is hindered by high data demands and thus low efficiency and scalability. Through independent improvements of components such as replay buffers or more stable learning algorithms, and through massively distributed…
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Artikel Jurnal Teks lengkap
Self-referencing embedded strings (SELFIES): A 100% robust molecular string representation
Abstract The discovery of novel materials and functional molecules can help to solve some of society’s most urgent challenges, ranging from efficient energy harvesting and storage to uncovering novel pharmaceutical drug candidates. Traditionally matter engineering–generally denoted as inverse…
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Artikel Jurnal
Interpretable delta-learning of GW quasiparticle energies from GGA-DFT
Accurate prediction of the ionization potential and electron affinity energies of small molecules are important for many applications. Density functional theory (DFT) is computationally inexpensive, but can be very inaccurate for frontier orbital energies or ionization energies. The GW method is…
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Artikel Jurnal
Accurate GW frontier orbital energies of 134 kilo molecules
Abstract HOMO and LUMO energies are critical molecular properties that typically require high accuracy computations for practical applicability. Until now, a comprehensive dataset containing sufficiently accurate HOMO and LUMO energies has been unavailable. In this study, we introduce a new dataset…
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Artikel Jurnal Teks lengkap
Shift and flip invariant CNNs for predicting laminar flow properties
The integration of machine learning into fluid dynamics has accelerated in recent years, driven by the proliferation of high-fidelity data and enhanced computational resources. Acting as efficient surrogate models for computationally intensive simulations, these data-driven approaches provide…
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Artikel Jurnal
Bayesian optimization approach to quantify the effect of input parameter uncertainty on predictions of numerical physics simulations
An understanding of how input parameter uncertainty in the numerical simulation of physical models leads to simulation output uncertainty is a challenging task. Common methods for quantifying output uncertainty, such as performing a grid or random search over the model input space, are…
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Artikel Jurnal
Machine learning for rapid discovery of laminar flow channel wall modifications that enhance heat transfer
Numerical simulation of fluid flow plays an essential role in modeling many physical phenomena, which enables technological advancements, contributes to sustainable practices, and expands our understanding of various natural and engineered systems. The calculation of heat transfer in fluid flow in…
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Artikel Jurnal
MOF Synthesis Prediction Enabled by Automatic Data Mining and Machine Learning**
Despite rapid progress in the field of metal-organic frameworks (MOFs), the potential of using machine learning (ML) methods to predict MOF synthesis parameters is still untapped. Here, we show how ML can be used for rationalization and acceleration of the MOF discovery process by directly…
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Artikel Jurnal
Infrared spectrum analysis of organic molecules with neural networks using standard reference data sets in combination with real-world data
Abstract In this study, we propose a neural network- based approach to analyze IR spectra and detect the presence of functional groups. Our neural network architecture is based on the concept of learning split representations. We demonstrate that our method achieves favorable validation performance…
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Artikel Jurnal Teks lengkap
PAL – parallel active learning for machine-learned potentials†
Constructing datasets representative of the target domain is essential for training effective machine learning models. Active learning (AL) is a promising method that iteratively extends training data to enhance model performance while minimizing data acquisition costs. However, current AL…