On simulating thin-film processes at the atomic scale using machine-learned force fields DOI
Suresh Kondati Natarajan, Jens Schneider, Neha Pandey

et al.

Journal of Vacuum Science & Technology A Vacuum Surfaces and Films, Journal Year: 2025, Volume and Issue: 43(3)

Published: March 24, 2025

Atomistic modeling of thin-film processes provides an avenue not only for discovering key chemical mechanisms the but also to extract quantitative metrics on events and reactions taking place at gas-surface interface. Molecular dynamics is a powerful computational method study evolution process atomic scale, studies industrially relevant usually require suitable force fields, which are, in general, available all interest. However, machine-learned fields (MLFFs) are conquering field materials surface science. In this paper, we demonstrate how efficiently build MLFFs simulations provide two examples technologically processes: precursor pulse layer deposition HfO2 etching MoS2.

Language: Английский

Experimental Energies of Formation Reactions for Adsorbates on Late Transition Metal Surfaces: a Database Update DOI
Charles T. Campbell, Jan Fingerhut, Alec M. Wodtke

et al.

Surface Science, Journal Year: 2025, Volume and Issue: unknown, P. 122714 - 122714

Published: Feb. 1, 2025

Language: Английский

Citations

0
On simulating thin-film processes at the atomic scale using machine-learned force fields DOI
Suresh Kondati Natarajan, Jens Schneider, Neha Pandey

et al.

Journal of Vacuum Science & Technology A Vacuum Surfaces and Films, Journal Year: 2025, Volume and Issue: 43(3)

Published: March 24, 2025

Atomistic modeling of thin-film processes provides an avenue not only for discovering key chemical mechanisms the but also to extract quantitative metrics on events and reactions taking place at gas-surface interface. Molecular dynamics is a powerful computational method study evolution process atomic scale, studies industrially relevant usually require suitable force fields, which are, in general, available all interest. However, machine-learned fields (MLFFs) are conquering field materials surface science. In this paper, we demonstrate how efficiently build MLFFs simulations provide two examples technologically processes: precursor pulse layer deposition HfO2 etching MoS2.

Language: Английский

Citations

0