@article { ISI:000427815800001,
	title = {Implanted neural network potentials: Application to Li-Si alloys},
	journal = {PHYSICAL REVIEW B},
	volume = {97},
	number = {9},
	year = {2018},
	month = {MAR 20},
	abstract = {Modeling the behavior of materials composed of elements with different bonding and electronic structure character for large spatial and temporal scales and over a large compositional range is a challenging problem. Cases in point are amorphous alloys of Si, a prototypical covalent material, and Li, a prototypical metal, which are being considered as anodes for high-energy-density batteries. To address this challenge, we develop a methodology based on neural networks that extends the conventional training approach to incorporate pre-trained parts that capture the character of different components, into the overall network; we refer to this model as the {\textquoteleft}{\textquoteleft}implanted neural network{\textquoteright}{\textquoteright} method. We show that this approach works well for the Si-Li amorphous alloys for a wide range of compositions, giving good results for key quantities like the diffusion coefficients. The method is readily generalizable to more complicated situations that involve two or more different elements.},
	issn = {2469-9950},
	doi = {10.1103/PhysRevB.97.094106},
	author = {Onat, Berk and Ekin D. Cubuk and Malone, Brad D. and Efthimios Kaxiras}
}
