Abstract Machine learning is playing an increasing role in the discovery of new materials
and may also facilitate the search for optimum growth conditions for crystals and thin films …

Deposition processes often involve surface and fluid-phase phenomena that are inherently
coupled but occur over different time and length scales. A multiscale integration hybrid …

Modeling the growth-front profile of an epitaxial film requires atomic-scale processes such
as adatom motion and step-adatom interactions to be folded into more 'coarse …

Understanding fundamental growth processes is key to the control of nonequilibrium
structure formation for a wide range of materials on all length scales, from atomic to …

Atomistic models are advancing our understanding of crystal growth at an increasing rate,
benefiting from rapidly increasing computational power. This chapter discusses Monte Carlo …

A new kinetic Monte Carlo algorithm that efficiently accounts for elastic strain is presented
and applied to study various phenomena that take place during heteroepitaxial growth. For …

We present a network modeling approach for various thin film growth techniques that
incorporates re-emitted particles due to the nonunity sticking coefficients. We model re …

A nonlinear differential equation is derived for the surface shape evolution in epitaxial
growth, from a transport equation for the adatoms. A negative Ehrlich-Schwöbel barrier is …

This chapter gives an introduction to the epitaxial growth of thin films on solid substrates.
The term epitaxy refers to the growth of a crystalline layer on (epi) the surface of a crystalline …

We have modeled molecular beam epitaxial growth by d= 2+ 1 solid-on-solid models using
dynamical Monte Carlo simulations. The chosen parameters are representative values of …