663 citations · 1.7k across the 24 of their papers we have counts for
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JARVIS-Leaderboard: A Large Scale Benchmark of Materials Design Methods
Kamal Choudhary, Daniel Wines, Kangming Li +35
Lack of rigorous reproducibility and validation are major hurdles for scientific development across many fields. Materials science in particular encompasses a variety of experiment…
Mitigating Molecular Aggregation in Drug Discovery with Predictive Insights from Explainable AI
Hunter Sturm, Jonas Teufel, Kaitlin A. Isfeld +2
Herein, we present the application of MEGAN, our explainable AI (xAI) model, for the identification of small colloidally aggregating molecules (SCAMs). This work offers solutions t…
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…
Accurate GW frontier orbital energies of 134 kilo molecules
Artem Fediai, Patrick Reiser, Jorge Enrique Olivares Peña +2
The QM9 dataset [Scientific Data, Vol. 1, 140022 (2014)] became a standard dataset to benchmark machine learning methods, especially on molecular graphs. It contains geometries as…