Multiscale Simulation of Organic Electronics Via Smart Scheduling of Quantum Mechanics Computations. - Pascal Friederich; Timo Strunk; Wolfgang Wenzel; Ivan Kondov (ICCS, 2016)

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)

Augmenting Genetic Algorithms with Deep Neural Networks for Exploring the Chemical Space. - AkshatKumar Nigam; Pascal Friederich; Mario Krenn; Alán Aspuru-Guzik (ICLR, 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)

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)

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)

Implementing graph neural networks with TensorFlow-Keras. - Patrick Reiser; André Eberhard; Pascal Friederich (CoRR, 2021)

Neural message passing on high order paths. - Daniel Flam-Shepherd; Tony C. Wu; Pascal Friederich; 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)

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)

Graph neural networks in TensorFlow-Keras with RaggedTensor representation (kgcnn). - Patrick Reiser; André Eberhard; Pascal Friederich (Softw. Impacts, 2021)

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)

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)

Graph neural networks to learn joint representations of disjoint molecular graphs. - Chen Shao; Zhou Chen; Pascal Friederich (CoRR, 2022)

Actively Learning Costly Reward Functions for Reinforcement Learning. - André Eberhard; Houssam Metni; Georg Fahland; Alexander Stroh; Pascal Friederich (CoRR, 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)

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; et al. (Patterns, 2022)

Quantifying the Intrinsic Usefulness of Attributional Explanations for Graph Neural Networks with Artificial Simulatability Studies. - Jonas Teufel; Luca Torresi; Pascal Friederich (xAI (2), 2023)

MEGAN: Multi-explanation Graph Attention Network. - Jonas Teufel; Luca Torresi; Patrick Reiser; Pascal Friederich (xAI (2), 2023)

Connectivity Optimized Nested Graph Networks for Crystal Structures. - Robin Ruff; Patrick Reiser; Jan Stühmer; Pascal Friederich (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)

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)

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., 2023)

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 (ICML, 2024)

Conditional Normalizing Flows for Active Learning of Coarse-Grained Molecular Representations. - Henrik Schopmans; Pascal Friederich (ICML, 2024)

Global Concept Explanations for Graphs by Contrastive Learning. - Jonas Teufel; Pascal Friederich (xAI (1), 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)

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)

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)

Temperature-Annealed Boltzmann Generators. - Henrik Schopmans; Pascal Friederich (CoRR, 2025)

Symmetry-Aware Bayesian Flow Networks for Crystal Generation. - Laura Ruple; Luca Torresi; Henrik Schopmans; Pascal Friederich (CoRR, 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; et al. (CoRR, 2025)

Improving Counterfactual Truthfulness for Molecular Property Prediction through Uncertainty Quantification. - Jonas Teufel; Annika Leinweber; Pascal Friederich (CoRR, 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; et al. (CoRR, 2025)

Learning Potential Energy Surfaces of Hydrogen Atom Transfer Reactions in Peptides. - Marlen Neubert; Patrick Reiser; Frauke Gräter; Pascal Friederich (CoRR, 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, 2025)