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Probing C84-embedded Si Substrate Using Scanning Probe Microscopy and Molecular Dynamics

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Screening of Material Defects using Universal Machine-Learning Interatomic Potentials.

Ethan Berger^1,2, Mohammad Bagheri³, Hannu-Pekka Komsa¹

¹Microelectronics Research Unit, Faculty of Information Technology and Electrical Engineering, University of Oulu, P.O. Box 4500, Oulu, FIN-90014, Finland.

Small (Weinheim an Der Bergstrasse, Germany)

|August 4, 2025

Summary

This summary is machine-generated.

Universal machine-learning interatomic potentials accelerate the discovery of novel materials. This study demonstrates their accuracy for screening defective materials and identifying new stable compounds, significantly advancing materials science.

Keywords:

2D materials benchmark defects machine‐learning interatomic potential vacancies

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Area of Science:

Materials Science
Computational Chemistry
Condensed Matter Physics

Background:

Computational modeling accelerates the discovery of new materials with desired properties.
Machine-learning interatomic potentials offer high accuracy at reduced computational cost.
Previous applications of these potentials have not explored screening defective materials.

Purpose of the Study:

To assess the accuracy of universal machine-learning interatomic potentials for large-scale screening of defective materials.
To explore the application of these potentials in discovering new stable compounds and simulating material properties.
To analyze the formation energies of vacancies in relation to oxidation numbers.

Main Methods:

Performed vacancy calculations for 86,259 materials using the Materials Project database.
Utilized universal machine-learning interatomic potentials for accurate and efficient simulations.
Analyzed formation energies and oxidation states to identify promising material candidates.

Main Results:

Demonstrated that machine-learning interatomic potentials are sufficiently accurate for large-scale defective material screening.
Identified new materials at or below the convex hull, indicating potential stability.
Successfully simulated the etching of low-dimensional materials, showcasing model versatility.

Conclusions:

Universal machine-learning interatomic potentials are a powerful tool for accelerating materials discovery, particularly for defective systems.
The study validates the use of these potentials for identifying novel stable compounds and understanding defect properties.
This approach opens new avenues for computational screening in materials science and engineering.