most citedApplication-specific machine-learned interatomic potentials: exploring the trade-off between DFT convergence, MLIP expressivity, and computational cost

5 citations · 5 across the 1 of their papers we have counts for

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1 paper

physics.comp-ph2025★ 5 cited

Application-specific machine-learned interatomic potentials: exploring the trade-off between DFT convergence, MLIP expressivity, and computational cost

Ilgar Baghishov, Jan Janssen, Graeme Henkelman +1

Machine-learned interatomic potentials (MLIPs) are revolutionizing computational materials science and chemistry by offering an efficient alternative to {\em ab initio} molecular d…