15 citations · 38 across the 10 of their papers we have counts for
3 papers · 1 filter
Resolving the Body-Order Paradox of Machine Learning Interatomic Potentials
Sanggyu Chong, Tong Jiang, Michelangelo Domina +4
In many cases, the predictions of machine learning interatomic potentials (MLIPs) can be interpreted as a sum of body-ordered contributions, which is explicit when the model is dir…
Uncertainty in the era of machine learning for atomistic modeling
Federico Grasselli, Sanggyu Chong, Venkat Kapil +2
The widespread adoption of machine learning surrogate models has significantly improved the scale and complexity of systems and processes that can be explored accurately and effici…
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…