601 citations · 1.2k across the 19 of their papers we have counts for
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physics.chem-ph2026
Clever Materials: When Models Identify Good Materials for the Wrong Reasons
Kevin Maik Jablonka
Machine learning can accelerate materials discovery. Models perform impressively on many benchmarks. However, strong benchmark performance does not imply that a model learned chemi…
physics.chem-ph2026
Beyond Learning on Molecules by Weakly Supervising on Molecules
Gordan Prastalo, Kevin Maik Jablonka
Molecular representations are inherently task-dependent, yet most pre-trained molecular encoders are not. Task conditioning promises representations that reorganize based on task d…
physics.chem-ph2022★ 292 cited
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…