Samsung Advanced Institute of Technology

Under thin film deposition, when used in conjunction with the semiconductor atomic layer

deposition (ALD) method, the choice of precursor determines the properties and quality of

the thin film. Organometallic precursors such as alkaline earth metals (Sr and Ba) and group

4 transition metals (Zr and Hf) with cyclopentadienyl and tetrakis (ethylmethylamino) ligands

have recently gained attention for their stable deposition within high-temperature windows

in the ALD. The design of organometallic precursors with an ab initio molecular dynamics

(AIMD) simulations-based approach ensures high accuracy but comes with significant

computational costs. In this study, we aim to develop a machine-learning interatomic

potential (MLIP) through moment tensor potential (MTP) for fast and accurate potential

development of Sr, Ba, Zr, and Hf precursors. To establish the reliable training database for

MTP construction, we conducted AIMD simulations on each precursor across a range of

temperature settings, resulting in a variety of atomic structures. Constructed MTPs enable

efficient utilization of molecular dynamics (MD) simulations as well as calculations that

achieve an accuracy that approximates density functional theory (DFT). MTP construction

coupled with active learning ensures that the MTP for each precursor is reliable and that

databases can be expanded. High prediction accuracy is demonstrated by a mean absolute

error (MAE) of less than 0.04 eV/atom in all structures. In addition, generalization

performance is confirmed for general structures (structures with the same chemical elements

but different proportions) and is extended to cluster structures. The constructed MTP exhibits

an MAE of less than 0.15 eV/atom, even for untrained cluster structures. These results

demonstrate adequate representation and scalability as a basis for the development of MLIPs

capable of atomic simulations of organometallic precursors under various thermodynamic conditions.