34 citations · 46 across the 7 of their papers we have counts for
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physics.chem-ph2024★ 34 cited
ArcaNN: automated enhanced sampling generation of training sets for chemically reactive machine learning interatomic potentials
Rolf David, Miguel de la Puente, Axel Gomez +3
The emergence of artificial intelligence has profoundly impacted computational chemistry, particularly through machine-learned potentials (MLPs), which offer a balance of accuracy…
physics.chem-ph2022★ 10 cited
Explicit models of motions to understand protein side-chain dynamics
Nicolas Bolik-Coulon, Olivier Languin-Cattoën, Diego Carnevale +5
Nuclear magnetic relaxation is widely used to probe protein dynamics. For decades, most analyses of relaxation in proteins have relied successfully on the model-free approach, forg…