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Machine learning potential for the Cu-W system
Combining the excellent thermal and electrical properties of Cu with the high abrasion resistance and thermal stability of W, Cu-W nanoparticle-reinforced metal matrix... -
Neural network potential for Zr-H
The introduction of Hydrogen (H) into Zirconium (Zr) influences many mechanical properties, especially due to low H solubility and easy formation of Zirconium hydride phases.... -
Using metadynamics to build neural network potentials for reactive events: th...
The study of chemical reactions in aqueous media is very important for its implications in several fields of science, from biology to industrial processes. However, modeling... -
Insights into water permeation through hBN nanocapillaries by ab initio machi...
Water permeation between stacked layers of hBN sheets forming 2D nanochannels is investigated using large-scale ab initio-quality molecular dynamics simulations. A... -
Simulating diffusion properties of solid-state electrolytes via a neural netw...
The recently published DeePMD model, based on a deep neural network architecture, brings the hope of solving the time-scale issue which often prevents the application of first...
