29 citations · 52 across the 4 of their papers we have counts for
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Machine-learning potentials for structurally and chemically complex MAB phases: strain hardening and ripplocation-mediated plasticity
Nikola Koutná, Shuyao Lin, Lars Hultman +2
Though offering unprecedented pathways to molecular dynamics (MD) simulations of technologically-relevant materials and conditions, machine-learning interatomic potentials (MLIPs)…
Machine-Learning Potentials Predict Orientation- and Mode-Dependent Fracture in Refractory Diborides
Shuyao Lin, Zhuo Chen, Rebecca Janknecht +5
Fracture toughness () and fracture strength () are key criteria in the selection and design of reliable ceramics. However, their experimental character…
Phase stability and mechanical property trends for MAB phases by high-throughput ab initio calculations
Nikola Koutná, Lars Hultman, Paul H. Mayrhofer +1
MAB phases (MABs) are atomically-thin laminates of ceramic/metallic-like layers, having made a breakthrough in the development of 2D materials. Though theoretically offering a vast…
Machine-learning potentials for nanoscale simulations of deformation and fracture: example of TiB ceramic
Shuyao Lin, Luis Casillas-Trujillo, Ferenc Tasnádi +4
Machine-learning interatomic potentials (MLIPs) offer a powerful avenue for simulations beyond length and timescales of ab initio methods. Their development for investigation of me…