34 citations · 78 across the 10 of their papers we have counts for
11 papers
Hyper-Dimensional Fingerprints as Molecular Representations
Jonas Teufel, Luca Torresi, André Eberhard +1
Computational molecular representations underpin virtual screening, property prediction, and materials discovery. Conventional fingerprints are efficient and deterministic but lose…
Multi-stage Bayesian optimisation for dynamic decision-making in self-driving labs
Luca Torresi, Pascal Friederich
Self-driving laboratories (SDLs) are combining recent technological advances in robotics, automation, and machine learning based data analysis and decision-making to perform autono…
A self-driving lab for solution-processed electrochromic thin films
Selma Dahms, Luca Torresi, Shahbaz Tareq Bandesha +5
Solution-processed electrochromic materials offer high potential for energy-efficient smart windows and displays. Their performance varies with material choice and processing condi…
Generative Models for Crystalline Materials
Houssam Metni, Laura Ruple, Lauren N. Walters +13
Understanding structure-property relationships in materials is fundamental in condensed matter physics and materials science. Over the past few years, machine learning (ML) has eme…
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…
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…
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…
An integrated system built for small-molecule semiconductors via high-throughput approaches
Jianchang Wu, Jiyun Zhang, Manman Hu +16
High-throughput synthesis of solution-processable structurally variable small-molecule semiconductors is both an opportunity and a challenge. A large number of diverse molecules pr…
What is missing in autonomous discovery: Open challenges for the community
Phillip M. Maffettone, Pascal Friederich, Sterling G. Baird +16
Self-driving labs (SDLs) leverage combinations of artificial intelligence, automation, and advanced computing to accelerate scientific discovery. The promise of this field has give…
MEGAN: Multi-Explanation Graph Attention Network
Jonas Teufel, Luca Torresi, Patrick Reiser +1
We propose a multi-explanation graph attention network (MEGAN). Unlike existing graph explainability methods, our network can produce node and edge attributional explanations along…
Graph neural networks for materials science and chemistry
Patrick Reiser, Marlen Neubert, André Eberhard +8
Machine learning plays an increasingly important role in many areas of chemistry and materials science, e.g. to predict materials properties, to accelerate simulations, to design n…