Pascal Friederich

According to our database, Pascal Friederich authored at least 45 papers between 2016 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2026

Perspective: Towards sustainable exploration of chemical spaces with machine learning.

Leonardo Medrano Sandonas, David Balcells, Anton Bochkarev, Jacqueline M. Cole, Volker L. Deringer, Werner Dobrautz, Adrian Ehrenhofer, Thorben Frank, Pascal Friederich, Rico Friedrich, Janine George, Luca Ghiringhelli, Alejandra Hinostroza Caldas, Veronika Jurásková, Hannes Kneiding, Yury Lysogorskiy, Johannes T. Margraf, Hanna Türk, O. Anatole von Lilienfeld, Milica Todorovic, Alexandre Tkatchenko, Mariana Rossi, Gianaurelio Cuniberti

CoRR, April, 2026

Efficient Training of Boltzmann Generators Using Off-Policy Log-Dispersion Regularization.

Henrik Schopmans, Christopher von Klitzing, Pascal Friederich

CoRR, February, 2026

Graph Recognition via Subgraph Prediction.

André Eberhard, Gerhard Neumann, Pascal Friederich

CoRR, January, 2026

2025

Infrared spectrum analysis of organic molecules with neural networks using standard reference data sets in combination with real-world data.

Dev Punjabi, Yu-Chieh Huang, Laura Holzhauer, Pierre Tremouilhac, Pascal Friederich, Nicole Jung, Stefan Bräse

J. Cheminformatics, December, 2025

Multi-stage Bayesian optimisation for dynamic decision-making in self-driving labs.

Luca Torresi, Pascal Friederich

CoRR, December, 2025

A self-driving lab for solution-processed electrochromic thin films.

Selma Dahms, Luca Torresi, Shahbaz Tareq Bandesha, Jan Hansmann, Holger Röhm, Alexander Colsmann, Marco Schott, Pascal Friederich

CoRR, December, 2025

Towards a fully differentiable digital twin for solar cells.

Marie Louise Schubert, Houssam Metni, Jan David Fischbach, Benedikt Zerulla, Marjan Krstic, Ulrich W. Paetzold, Seyedamir Orooji, Olivier J. J. Ronsin, Yasin Ameslon, Jens Harting, Thomas Kirchartz, Sandheep Ravishankar, Chris Dreessen, Eunchi Kim, Christian Sprau, Mohamed Hussein, Alexander Colsmann, Karen Forberich, Klaus Jäger, Pascal Friederich, Carsten Rockstuhl

CoRR, December, 2025

Generative models for crystalline materials.

Houssam Metni, Laura Ruple, Lauren N. Walters, Luca Torresi, Jonas Teufel, Henrik Schopmans, Jona Östreicher, Yumeng Zhang, Marlen Neubert, Yuri Koide, Kevin Steiner, Paul Link, Lukas Bär, Mariana Petrova, Gerbrand Ceder, Pascal Friederich

CoRR, November, 2025

Learning Boltzmann Generators via Constrained Mass Transport.

Christopher von Klitzing, Denis Blessing, Henrik Schopmans, Pascal Friederich, Gerhard Neumann

CoRR, October, 2025

Learning Potential Energy Surfaces of Hydrogen Atom Transfer Reactions in Peptides.

Marlen Neubert, Patrick Reiser, Frauke Gräter, Pascal Friederich

CoRR, August, 2025

Predicting New Research Directions in Materials Science using Large Language Models and Concept Graphs.

Thomas Marwitz, Alexander Colsmann, Ben Breitung, Christoph Brabec, Christoph Kirchlechner, Eva Blasco, Gabriel Cadilha Marques, Horst Hahn, Michael Hirtz, Pavel A. Levkin, Yolita M. Eggeler, Tobias Schlöder, Pascal Friederich

CoRR, June, 2025

opXRD: Open Experimental Powder X-ray Diffraction Database.

Daniel Hollarek, Henrik Schopmans, Jona Östreicher, Jonas Teufel, Bin Cao, Adie Alwen, Simon Schweidler, Mriganka Singh, Tim Kodalle, Hanlin Hu, Gregoire Heymans, Maged Abdelsamie, Arthur Hardiagon, Alexander Wieczorek, Siarhei Zhuk, Ruth Schwaiger, Sebastian Siol, François-Xavier Coudert, Moritz Wolf, Carolin M. Sutter-Fella, Ben Breitung, Andrea M. Hodge, Tong-yi Zhang, Pascal Friederich

CoRR, March, 2025

Symmetry-Aware Bayesian Flow Networks for Crystal Generation.

Laura Ruple, Luca Torresi, Henrik Schopmans, Pascal Friederich

CoRR, February, 2025

The Black Hole Strategy: Gravity-Based Representative Sampling for Frugal Graph Learning on Metal-Organic Framework Networks.

Mehrdad Jalali, A. D. Dinga Wonanke, Pascal Friederich, Christof Wöll

J. Chem. Inf. Model., 2025

Advancing Aqueous Solubility Prediction: A Machine Learning Approach for Organic Compounds Using a Curated Data Set.

Mushtaq Ali, Sylvia Vanderheiden, Christoph W. Grathwol, Konrad Krämer, Pascal Friederich, Nicole Jung, Stefan Bräse

J. Chem. Inf. Model., 2025

Improving Counterfactual Truthfulness for Molecular Property Prediction Through Uncertainty Quantification.

Jonas Teufel, Annika Leinweber, Pascal Friederich

Proceedings of the Explainable Artificial Intelligence, 2025

Temperature-Annealed Boltzmann Generators.

Henrik Schopmans, Pascal Friederich

Proceedings of the Forty-second International Conference on Machine Learning, 2025

2024

Actively learning costly reward functions for reinforcement learning.

André Eberhard, Houssam Metni, Georg Fahland, Alexander Stroh, Pascal Friederich

Mach. Learn. Sci. Technol., 2024

EDITORIAL: Chemical Compound Space Exploration by Multiscale High-Throughput Screening and Machine Learning.

Ganna Gryn'ova, Tristan Bereau, Carolin Müller, Pascal Friederich, Rebecca C. Wade, Ariane Nunes-Alves, Thereza A. Soares, Kenneth M. Merz Jr.

J. Chem. Inf. Model., 2024

PAL - Parallel active learning for machine-learned potentials.

Chen Zhou, Marlen Neubert, Yuri Koide, Yumeng Zhang, Van-Quan Vuong, Tobias Schlöder, Stefanie Dehnen, Pascal Friederich

CoRR, 2024

Global Concept Explanations for Graphs by Contrastive Learning.

Jonas Teufel, Pascal Friederich

Proceedings of the Explainable Artificial Intelligence, 2024

Conditional Normalizing Flows for Active Learning of Coarse-Grained Molecular Representations.

Henrik Schopmans, Pascal Friederich

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Contextualized Policy Recovery: Modeling and Interpreting Medical Decisions with Adaptive Imitation Learning.

Jannik Deuschel, Caleb Ellington, Yingtao Luo, Benjamin J. Lengerich, Pascal Friederich, Eric P. Xing

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Interpretable delta-learning of GW quasiparticle energies from GGA-DFT.

Artem Fediai, Patrick Reiser, Jorge Enrique Olivares Peña, Wolfgang Wenzel, Pascal Friederich

Mach. Learn. Sci. Technol., September, 2023

Mitigating Molecular Aggregation in Drug Discovery with Predictive Insights from Explainable AI.

Hunter Sturm, Jonas Teufel, Kaitlin A. Isfeld, Pascal Friederich, Rebecca L. Davis

CoRR, 2023

Neural networks trained on synthetically generated crystals can extract structural information from ICSD powder X-ray diffractograms.

Henrik Schopmans, Patrick Reiser, Pascal Friederich

CoRR, 2023

Connectivity Optimized Nested Graph Networks for Crystal Structures.

Robin Ruff, Patrick Reiser, Jan Stühmer, Pascal Friederich

CoRR, 2023

MEGAN: Multi-explanation Graph Attention Network.

Jonas Teufel, Luca Torresi, Patrick Reiser, Pascal Friederich

Proceedings of the Explainable Artificial Intelligence, 2023

Quantifying the Intrinsic Usefulness of Attributional Explanations for Graph Neural Networks with Artificial Simulatability Studies.

Jonas Teufel, Luca Torresi, Pascal Friederich

Proceedings of the Explainable Artificial Intelligence, 2023

2022

SELFIES and the future of molecular string representations.

Mario Krenn, Qianxiang Ai, Senja Barthel, Nessa Carson, Angelo Frei, Nathan C. Frey, Pascal Friederich, Théophile Gaudin, Alberto Alexander Gayle, Kevin Maik Jablonka, Rafael F. Lameiro, Dominik Lemm, Alston Lo, Seyed Mohamad Moosavi, José Manuel Nápoles-Duarte, AkshatKumar Nigam, Robert Pollice, Kohulan Rajan, Ulrich Schatzschneider, Philippe Schwaller, Marta Skreta, Berend Smit, Felix Strieth-Kalthoff, Chong Sun, Gary Tom, Guido Falk von Rudorff, Andrew Wang, Andrew D. White, Adamo Young, Rose Yu, Alán Aspuru-Guzik

Patterns, 2022

Updated Calibrated Model for the Prediction of Molecular Frontier Orbital Energies and Its Application to Boron Subphthalocyanines.

Devon P. Holst, Pascal Friederich, Alán Aspuru-Guzik, Timothy P. Bender

J. Chem. Inf. Model., 2022

Graph neural networks to learn joint representations of disjoint molecular graphs.

Chen Shao, Zhou Chen, Pascal Friederich

CoRR, 2022

Graph neural networks for materials science and chemistry.

Patrick Reiser, Marlen Neubert, André Eberhard, Luca Torresi, Chen Zhou, Chen Shao, Houssam Metni, Clint van Hoesel, Henrik Schopmans, Timo Sommer, Pascal Friederich

CoRR, 2022

On scientific understanding with artificial intelligence.

Mario Krenn, Robert Pollice, Si Yue Guo, Matteo Aldeghi, Alba Cervera-Lierta, Pascal Friederich, Gabriel dos Passos Gomes, Florian Häse, Adrian Jinich, AkshatKumar Nigam, Zhenpeng Yao, Alán Aspuru-Guzik

CoRR, 2022

2021

Graph neural networks in TensorFlow-Keras with RaggedTensor representation (kgcnn).

Patrick Reiser, André Eberhard, Pascal Friederich

Softw. Impacts, 2021

The influence of sorbitol doping on aggregation and electronic properties of PEDOT: PSS: a theoretical study.

Pascal Friederich, Salvador León, José Darío Perea, Loïc M. Roch, Alán Aspuru-Guzik

Mach. Learn. Sci. Technol., 2021

Scientific intuition inspired by machine learning-generated hypotheses.

Pascal Friederich, Mario Krenn, Isaac Tamblyn, Alán Aspuru-Guzik

Mach. Learn. Sci. Technol., 2021

Neural message passing on high order paths.

Daniel Flam-Shepherd, Tony C. Wu, Pascal Friederich, Alán Aspuru-Guzik

Mach. Learn. Sci. Technol., 2021

Implementing graph neural networks with TensorFlow-Keras.

Patrick Reiser, André Eberhard, Pascal Friederich

CoRR, 2021

Analyzing dynamical disorder for charge transport in organic semiconductors via machine learning.

Patrick Reiser, Manuel Konrad, Artem Fediai, Salvador León, Wolfgang Wenzel, Pascal Friederich

CoRR, 2021

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

Matthias Schniewind, Alexander Stroh, Bradley P. Ladewig, Pascal Friederich

CoRR, 2021

2020

Self-referencing embedded strings (SELFIES): A 100% robust molecular string representation.

Mario Krenn, Florian Häse, AkshatKumar Nigam, Pascal Friederich, Alán Aspuru-Guzik

Mach. Learn. Sci. Technol., 2020

Augmenting Genetic Algorithms with Deep Neural Networks for Exploring the Chemical Space.

AkshatKumar Nigam, Pascal Friederich, Mario Krenn, Alán Aspuru-Guzik

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

SELFIES: a robust representation of semantically constrained graphs with an example application in chemistry.

Mario Krenn, Florian Häse, AkshatKumar Nigam, Pascal Friederich, Alán Aspuru-Guzik

CoRR, 2019

2016

Multiscale Simulation of Organic Electronics Via Smart Scheduling of Quantum Mechanics Computations.

Pascal Friederich, Timo Strunk, Wolfgang Wenzel, Ivan Kondov

Proceedings of the International Conference on Computational Science 2016, 2016