Publications (47)
Discovering one molecule out of a million: inverse design of molecular hole transporting semiconductors tailored for perovskite solar cells
Jianchang Wu, Luca Torresi, ManMan Hu +17
The inverse design of tailored organic molecules for specific optoelectronic devices of high complexity holds an enormous potential, but has not yet been realized1,2. The complexit…
Symmetry-Aware Bayesian Flow Networks for Crystal Generation
Laura Ruple, Luca Torresi, Henrik Schopmans +1
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 se…
Contextualized Policy Recovery: Modeling and Interpreting Medical Decisions with Adaptive Imitation Learning
Jannik Deuschel, Caleb N. Ellington, Yingtao Luo +3
Interpretable policy learning seeks to estimate intelligible decision policies from observed actions; however, existing models force a tradeoff between accuracy and interpretabilit…
Machine learning for rapid discovery of laminar flow channel wall modifications that enhance heat transfer
Yuri Koide, Arjun J. Kaithakkal, Matthias Schniewind +3
Numerical simulation of fluids plays an essential role in modeling many physical phenomena, which enables technological advancements, contributes to sustainable practices, and expa…
SELFIES and the future of molecular string representations
Mario Krenn, Qianxiang Ai, Senja Barthel +28
Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad applications to challenging tasks in chemistry and materials science. Examples include…
Quantifying the Intrinsic Usefulness of Attributional Explanations for Graph Neural Networks with Artificial Simulatability Studies
Jonas Teufel, Luca Torresi, Pascal Friederich
Despite the increasing relevance of explainable AI, assessing the quality of explanations remains a challenging issue. Due to the high costs associated with human-subject experimen…