Luca Brugnoli

Influence of Exchange–Correlation Functional on Machine-Learned Interatomic Potentials’ Accuracy: A Systematic Study of Borosilicate Glasses

Abstract Machine-learned interatomic potentials (MLIPs) have emerged as a strategy to accelerate molecular simulations, offering the promise of quantum chemical accuracy at a cost close to that of classical force fields. They are commonly trained on reference data obtained at the...

Overcoming Sampling Limitations Using Machine-Learned Interatomic Potentials: The Case of Water-in-Salt Electrolytes

Abstract Machine-learned interatomic potentials hold the promise to enable the modeling of highly concentrated liquids over meaningful time scales, far from reach for current ab initio electronic structure methods. Here we evaluate the performances of various MACE potentials in m...