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Atomistic Insights into MOCVD Growth of GaAs using a Closed Loop Autonomous System Combining Directed Reinforcement Learning and Kinetic Monte Carlo Simulations — arXiv:2611.01646

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.01646
License (as archived): CC0-1.0
Archived: 2026-11-03T11:49:51.325Z
SHA-256 of the archived text: aa15bb3d529a5721fc2b3a60284e07b4bd428974fe24e7bd75d56cf2b40e034e
Stable URL of this page: https://metraipolis.com/documento/dc_da558b5618cd

How to cite

Atomistic Insights into MOCVD Growth of GaAs using a Closed Loop Autonomous System Combining Directed Reinforcement Learning and Kinetic Monte Carlo Simulations — arXiv:2611.01646 — arXiv (en), https://arxiv.org/abs/2611.01646 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_da558b5618cd (Metraipolis archive dc_da558b5618cd, sha256 aa15bb3d529a5721fc2b3a60284e07b4bd428974fe24e7bd75d56cf2b40e034e)

BibTeX

@misc{dcda558b5618cd,
  title = {Atomistic Insights into MOCVD Growth of GaAs using a Closed Loop Autonomous System Combining Directed Reinforcement Learning and Kinetic Monte Carlo Simulations — arXiv:2611.01646},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.01646},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_da558b5618cd; sha256 aa15bb3d529a5721fc2b3a60284e07b4bd428974fe24e7bd75d56cf2b40e034e},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.01646. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Conventional process optimization of the Metal-Organic Chemical Vapor Deposition (MOCVD) growth parameters to achieve high-quality thin films relies on iterative, resource-intensive experimental runs. While predictive modeling can circumvent this cost and effort, simulating thin-film growth is constrained by the vastly different length and time scales of the deposition process. While continuum (e.g., Computational Fluid Dynamics (CFD)) and first-principles methods (e.g., Molecular Dynamics (MD)) struggle to balance experimental length/time scales and atomistic accuracy, Kinetic Monte Carlo (KMC) bridges this divide by resolving atomistic events whilst remaining tractable at experimental length and time scales. However, the accuracy of KMC relies on kinetic rate parameters that require costly density functional theory (DFT) calculations. In order to circumvent this, we present a framework that calibrates the rate parameters directly against experimental growth data. A forward KMC growth model is coupled in a closed loop with a Monte Carlo Tree Search (MCTS) optimizer that navigates the high-dimensional parameter space, with the search steered by the degree of rate control of each reaction rate parameter. The approach invokes no material-specific assumptions and hence, any deposition process whose forward kinetics can be reduced to a set of rate-controlled events is amenable to the same treatment. We demonstrate it for the MOCVD of GaAs, where the calibrated model captures the characteristic temperature dependence of the measured growth rate. The framework is also able to recover the three well known kinetic regimes characterized by change in the rate determining step from precursor chemisorption to desorption. As a result, the framework converts routinely measured growth data into a mechanistically interpretable model of the deposition process.

Tema degli esploratori: fisica e astronomia (elenco arXiv physics.comp-ph, nuovi lavori del giorno).
Categorie arXiv: Materials Science (cond-mat.mtrl-sci) ; Computational Physics (physics.comp-ph).
Pagina del lavoro: https://arxiv.org/abs/2611.01646 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-03T11:10:22.283Z dalla pagina https://arxiv.org/list/physics.comp-ph/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).