2 citations · 2 across the 2 of their papers we have counts for
2 papers
cs.LG2024★ 2 cited
: A Parameter-Efficient Foundation Model for Molecular Learning
Kerstin Kläser, Błażej Banaszewski, Samuel Maddrell-Mander +5
In biological tasks, data is rarely plentiful as it is generated from hard-to-gather measurements. Therefore, pre-training foundation models on large quantities of available data a…
cs.LG2024
Reducing the Cost of Quantum Chemical Data By Backpropagating Through Density Functional Theory
Alexander Mathiasen, Hatem Helal, Paul Balanca +6
Density Functional Theory (DFT) accurately predicts the quantum chemical properties of molecules, but scales as . Schütt et al. (2019) successfully appro…