BCSeg

BC Model-inspired segregation energy (Eseg) model for single atom alloy (SAA) nanomaterials.

• Trained on periodic slab SAA DFT data (plot below)
• Applicable to nanoparticle SAAs

Reproduce parity plot by running python eseg_model.py

Easily predict Eseg with a few lines of code

from eseg_model import get_eseg_model
model = get_eseg_model()
predicted_eseg = model.predict(X)

See eseg_model.py and example.py for additional details

Features

BCSeg utilizes 5 simple, host- and dopant-based features that avoid costly calculations, like Density Functional Theory.

ΔCE / CN

• ΔCE = CE^host - CE^dopant
• CEⁱ = cohesive energy of monometallic material (eV)
  • For periodic slabs, CE = CE_bulk
  • For nanoparticles, CE = CE_NP
• CN = coordination number (number of bonds)

χ^host

• Gordy electronegativity of the host

ΔEA

• EA = electron affinity (eV)
• ΔEA = EA^host - EA^dopant

r^dopant

• atomic radius of the dopant (Å)

IP^dopant

• ionization potential (IP) of the dopant (eV)