112 citations · 237 across the 19 of their papers we have counts for
5 papers · 1 filter
Aluminum solidification and nanopolycrystal deformation via a Graph Neural Network Potential and Million-Atom Simulations
Ian Störmer, Julija Zavadlav
Solidification governs the microstructure and, therefore, the mechanical response of metal components, yet the atomistic details of nucleation and defect formation are often diffic…
Refining Machine Learning Potentials through Thermodynamic Theory of Phase Transitions
Paul Fuchs, Julija Zavadlav
Foundational Machine Learning Potentials can resolve the accuracy and transferability limitations of classical force fields. They enable microscopic insights into material behavior…
chemtrain-deploy: A parallel and scalable framework for machine learning potentials in million-atom MD simulations
Paul Fuchs, Weilong Chen, Stephan Thaler +1
Machine learning potentials (MLPs) have advanced rapidly and show great promise to transform molecular dynamics (MD) simulations. However, most existing software tools are tied to…
Accelerated Simulations of Molecular Systems through Learning of their Effective Dynamics
Pantelis R. Vlachas, Julija Zavadlav, Matej Praprotnik +1
Simulations are vital for understanding and predicting the evolution of complex molecular systems. However, despite advances in algorithms and special purpose hardware, accessing t…
Bayesian selection for coarse-grained models of liquid water
Julija Zavadlav, Georgios Arampatzis, Petros Koumoutsakos
The necessity for accurate and computationally efficient representations of water in atomistic simulations that can span biologically relevant timescales has born the necessity of…