Search Results - "Friederich, Pascal"

Symmetry-aware Bayesian flow networks for crystal generation.

Authors: Ruple, Laura Torresi, Luca Schopmans, Henrik et al.

Source: NPJ Computational Materials; 5/19/2026, Vol. 12 Issue 1, p1-8, 8p

28‐1: Invited Paper: Bottom‐Up OLED Development by Virtual Design: Systematic Elimination of Performance Bottlenecks Using a Microscopic Simulation Approach.

Authors: Neumann, Tobias Symalla, Franz Strunk, Timo et al.

Source: SID Symposium Digest of Technical Papers; Jun2022, Vol. 53 Issue 1, p322-325, 4p

Tuning the Transparency and Exciton Transition of D‐π‐A‐π‐D Type Small Molecules.

Authors: Aydan Alkan, Ecem Metni, Houssam Reiser, Patrick et al.

Source: Chemistry - A European Journal; 8/13/2025, Vol. 31 Issue 45, p1-10, 10p

Mitigating Molecular Aggregation in Drug Discovery With Predictive Insights From Explainable AI.

Authors: Sturm, Hunter Teufel, Jonas Isfeld, Kaitlin A. et al.

Source: Angewandte Chemie International Edition; 7/14/2025, Vol. 64 Issue 29, p1-10, 10p

87‐3: Development of a CAE‐Based OLED Modeling Environment for Electrical and Optical Simulation.

Authors: Park, Han Wool Choi, Yoon Ji Yang, Han-Seung et al.

Source: SID Symposium Digest of Technical Papers; Jun2025, Vol. 56 Issue 1, p1197-1199, 3p

Functional Material Systems Enabled by Automated Data Extraction and Machine Learning.

Authors: Kalhor, Payam Jung, Nicole Bräse, Stefan et al.

Source: Advanced Functional Materials; May2024, Vol. 34 Issue 20, p1-11, 11p

JARVIS-Leaderboard: a large scale benchmark of materials design methods.

Authors: Choudhary, Kamal Wines, Daniel Li, Kangming et al.

Source: NPJ Computational Materials; 5/7/2024, Vol. 10 Issue 1, p1-17, 17p

Shift and flip invariant CNNs for predicting laminar flow properties.

Authors: Koide, Yuri Teufel, Jonas Torresi, Luca et al.

Source: APL Machine Learning. Mar2026, Vol. 4 Issue 1, p1-12. 12p.

Lattice Metamaterials with Mesoscale Motifs: Exploration of Property Charts by Bayesian Optimization.

Authors: Kulagin, Roman Reiser, Patrick Truskovskyi, Kyryl et al.

Source: Advanced Engineering Materials; Jul2023, Vol. 25 Issue 13, p1-8, 8p

Uncertainty quantification enables reliable deep learning for protein–ligand binding affinity prediction.

Authors: Rayka, Milad Naghavi, S. Shahab

Source: Scientific Reports. 12/4/2025, Vol. 15 Issue 1, p1-11. 11p.

Synthesis and Characterization of High‐Entropy CrMoNbTaVW Thin Films Using High‐Throughput Methods.

Authors: Schweidler, Simon Schopmans, Henrik Reiser, Patrick et al.

Source: Advanced Engineering Materials; Feb2023, Vol. 25 Issue 2, p1-7, 7p

Particle detection by means of neural networks and synthetic training data refinement in defocusing particle tracking velocimetry.

Authors: Dreisbach, Maximilian Leister, Robin Probst, Matthias et al.

Source: Measurement Science & Technology; Dec2022, Vol. 33 Issue 12, p1-17, 17p

MOF Synthesis Prediction Enabled by Automatic Data Mining and Machine Learning**.

Authors: Luo, Yi Bag, Saientan Zaremba, Orysia et al.

Source: Angewandte Chemie International Edition; 5/2/2022, Vol. 61 Issue 19, p1-5, 5p

Vorhersage der MOF‐Synthese durch automatisches Data‐Mining und maschinelles Lernen**.

Authors: Luo, Yi Bag, Saientan Zaremba, Orysia et al.

Source: Angewandte Chemie; 5/2/2022, Vol. 134 Issue 19, p1-6, 6p

26‐4: Computer‐Aided Optimization of Multilayer OLED Devices.

Authors: Symalla, Franz Friederich, Pascal Kaiser, Simon et al.

Source: SID Symposium Digest of Technical Papers; May2018, Vol. 49 Issue 1, p340-342, 3p

Concentration dependent energy levels shifts in donor-acceptor mixtures due to intermolecular electrostatic interaction.

Authors: Bag, Saientan Friederich, Pascal Kondov, Ivan et al.

Source: Scientific Reports. 8/27/2019, Vol. 9 Issue 1, pN.PAG-N.PAG. 1p.

Ab initio modeling of steady-state and time-dependent charge transport in hole-only a-NPD devices.

Authors: Liu, Feilong Massé, Andrea Friederich, Pascal et al.

Source: Applied Physics Letters; 12/12/2016, Vol. 109 Issue 24, p1-4, 4p, 3 Graphs

Machine learning for rapid discovery of laminar flow channel wall modifications that enhance heat transfer.

Authors: Koide, Yuri Kaithakkal, Arjun J. Schniewind, Matthias et al.

Source: APL Machine Learning. Mar2024, Vol. 2 Issue 1, p1-12. 12p.

Bayesian optimization approach to quantify the effect of input parameter uncertainty on predictions of numerical physics simulations.

Authors: McCallum, Samuel G. Lerpinière, James E. Jensen, Kjeld O. et al.

Source: APL Machine Learning. Dec2023, Vol. 1 Issue 4, p1-7. 7p.

Multiscale Simulation of Photoluminescence Quenching in Phosphorescent OLED Materials.

Authors: Symalla, Franz Heidrich, Shahriar Friederich, Pascal et al.

Source: Advanced Theory & Simulations. Apr2020, Vol. 3 Issue 4, p1-10. 10p.