66 citations · 109 across the 11 of their papers we have counts for
3 papers · 1 filter
Prediction rigidities for data-driven chemistry
Sanggyu Chong, Filippo Bigi, Federico Grasselli +3
The widespread application of machine learning (ML) to the chemical sciences is making it very important to understand how the ML models learn to correlate chemical structures with…
i-PI 3.0: a flexible and efficient framework for advanced atomistic simulations
Yair Litman, Venkat Kapil, Yotam M. Y. Feldman +13
Atomic-scale simulations have progressed tremendously over the past decade, largely due to the availability of machine-learning interatomic potentials. These potentials combine the…
Uncertainty quantification by direct propagation of shallow ensembles
Matthias Kellner, Michele Ceriotti
Statistical learning algorithms provide a generally-applicable framework to sidestep time-consuming experiments, or accurate physics-based modeling, but they introduce a further so…