22 citations · 42 across the 8 of their papers we have counts for
4 papers · 1 filter
AutoPot: Automated and massively parallelized construction of Machine-Learning Potentials
Max Hodapp, Guillaume Anciaux
Machine-learning potentials (MLIPs) have been a breakthrough for computational physics in bringing the accuracy of quantum mechanics to atomistic modeling. To achieve near-quantum…
Exact average many-body interatomic interaction model for random alloys
Max Hodapp
Understanding the physical origin of deformation mechanisms in random alloys requires an understanding of their average behavior and, equally important, the role of local fluctuati…
Automated atomistic simulations of dissociated dislocations with ab initio accuracy
Laura Mismetti, Max Hodapp
In (M Hodapp and A Shapeev 2020 Mach. Learn.: Sci. Technol. 1 045005), we have proposed an algorithm that fully automatically trains machine-learning interatomic potentials (MLIPs)…
In operando active learning of interatomic interaction during large-scale simulations
Max Hodapp, Alexander Shapeev
A well-known drawback of state-of-the-art machine-learning interatomic potentials is their poor ability to extrapolate beyond the training domain. For small-scale problems with ten…