Sorption Molecular Dynamics of Point Defects in 2D Materials with a Machine Learning Potential
Abstract
Defect engineering is a crucial tool for tuning and tailoring the properties of two-dimensional (2D) materials. A key challenge is identifying which defects are kinetically stable at room temperature and sufficiently promising to warrant further investigation. However, identifying stable defects with suitable properties using first-principles calculations, such as density functional theory (DFT) is computationally demanding, effectively limiting the scope of high-throughput studies. In this work, we investigate whether the machine learning interatomic potential MACE can reproduce sorption trends (adsorption vs. absorption) previously obtained from high-throughput DFT calculations and extend the analysis to elevated temperatures. A total of 17,624 defect structures from the ADAQ database are simulated using MACE-accelerated molecular dynamics (MD) at both 0 K and 300 K. The results show that MACE successfully reproduces the overall sorption trends observed in DFT calculations at 0 K at a fraction of the computational cost. Simulations performed at 300 K further indicate that the qualitative sorption trends remain largely the same when including finite-temperature effects. These findings demonstrate that MACE-driven MD can be used as an effective high-throughput prescreening tool for defect-related studies in 2D materials, specially related to sorption. While conventional first-principles methods, such as DFT, remain necessary for accurate and quantitative validation, the use of MLIPs enables MD simulations of finite-temperature defect behavior at a fraction of the computational cost of DFT-based MD.
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BibTeX
@misc{skogemyr2026sorption,
title = {{Sorption Molecular Dynamics of Point Defects in 2D Materials with a Machine Learning Potential}},
author = {Nils-Ruben Skogemyr and Albin Sjöstedt and Isak Wernroth and Isak Häggqvist and Isac Ljungwald and Hans-Ove Åkesson and Henricus R. A. ten Eikelder and Rickard Armiento and Joel Davidsson},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.20063},
eprint = {2610.20063},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.20063},
}
RIS
TY - UNPB
TI - Sorption Molecular Dynamics of Point Defects in 2D Materials with a Machine Learning Potential
AU - Nils-Ruben Skogemyr
AU - Albin Sjöstedt
AU - Isak Wernroth
AU - Isak Häggqvist
AU - Isac Ljungwald
AU - Hans-Ove Åkesson
AU - Henricus R. A. ten Eikelder
AU - Rickard Armiento
AU - Joel Davidsson
PY - 2026
DA - 2026/10/14
PB - arXiv
DO - 10.48550/arxiv.2610.20063
UR - https://arxiv.org/abs/2610.20063
AB - Defect engineering is a crucial tool for tuning and tailoring the properties of two-dimensional (2D) materials. A key challenge is identifying which defects are kinetically stable at room temperature and sufficiently promising to warrant further investigation. However, identifying stable defects with suitable properties using first-principles calculations, such as density functional theory (DFT) is computationally demanding, effectively limiting the scope of high-throughput studies. In this work, we investigate whether the machine learning interatomic potential MACE can reproduce sorption trends (adsorption vs. absorption) previously obtained from high-throughput DFT calculations and extend the analysis to elevated temperatures. A total of 17,624 defect structures from the ADAQ database are simulated using MACE-accelerated molecular dynamics (MD) at both 0 K and 300 K. The results show that MACE successfully reproduces the overall sorption trends observed in DFT calculations at 0 K at a fraction of the computational cost. Simulations performed at 300 K further indicate that the qualitative sorption trends remain largely the same when including finite-temperature effects. These findings demonstrate that MACE-driven MD can be used as an effective high-throughput prescreening tool for defect-related studies in 2D materials, specially related to sorption. While conventional first-principles methods, such as DFT, remain necessary for accurate and quantitative validation, the use of MLIPs enables MD simulations of finite-temperature defect behavior at a fraction of the computational cost of DFT-based MD.
ER -