Machine-learning potentials for nanoscale simulations of tensile deformation and fracture in ceramics
Abstract Machine-learning interatomic potentials (MLIPs) offer a powerful avenue for
simulations beyond length and timescales of ab initio methods. Their development for …
simulations beyond length and timescales of ab initio methods. Their development for …
Automated ab initio-accurate atomistic simulations of dissociated dislocations
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 …
proposed an algorithm that fully automatically trains machine-learning interatomic potentials …
Automated atomistic simulations of dissociated dislocations with ab initio accuracy
In a previous work [M. Hodapp and A. Shapeev, Mach. Learn.: Sci. Technol. 1, 045005
(2020) 2632-2153 10.1088/2632-2153/aba373], we proposed an algorithm that fully …
(2020) 2632-2153 10.1088/2632-2153/aba373], we proposed an algorithm that fully …
Machine-learning potentials for nanoscale simulations of deformation and fracture: example of TiB ceramic
Machine-learning interatomic potentials (MLIPs) offer a powerful avenue for simulations
beyond length and timescales of ab initio methods. Their development for investigation of …
beyond length and timescales of ab initio methods. Their development for investigation of …
Physics-Transfer Learning for Material Strength Screening
The strength of materials, like many problems in the natural sciences, spans multiple length
and time scales, and the solution has to balance accuracy and performance. Peierls stress is …
and time scales, and the solution has to balance accuracy and performance. Peierls stress is …