4 citations · 4 across the 1 of their papers we have counts for
5 papers
Neural Message Passing on High Order Paths
Daniel Flam-Shepherd, Tony Wu, Pascal Friederich +1
Graph neural network have achieved impressive results in predicting molecular properties, but they do not directly account for local and hidden structures in the graph such as func…
From absorption spectra to charge transfer in PEDOT nanoaggregates with machine learning
Loïc M. Roch, Semion K. Saikin, Florian Häse +4
Fast and inexpensive characterization of materials properties is a key element to discover novel functional materials. In this work, we suggest an approach employing three classes…
Augmenting Genetic Algorithms with Deep Neural Networks for Exploring the Chemical Space
AkshatKumar Nigam, Pascal Friederich, Mario Krenn +1
Challenges in natural sciences can often be phrased as optimization problems. Machine learning techniques have recently been applied to solve such problems. One example in chemistr…
The influence of impurities on the charge carrier mobility of small molecule organic semiconductors
Pascal Friederich, Artem Fediai, Jing Li +10
Amorphous organic semiconductors based on small molecules and polymers are used in many applications, most prominently organic light emitting diodes (OLEDs) and organic solar cells…
Self-Referencing Embedded Strings (SELFIES): A 100% robust molecular string representation
Mario Krenn, Florian Häse, AkshatKumar Nigam +2
The discovery of novel materials and functional molecules can help to solve some of society's most urgent challenges, ranging from efficient energy harvesting and storage to uncove…