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Machine Learning Interatomic Potentials Enable Molecular Dynamics Simulations of Doped MoS₂.

Abrar Faiyad¹, Ashlie Martini¹

¹University of California Merced, Merced, California 95343, United States.

Journal of Chemical Theory and Computation

|February 24, 2026

Summary

This study validates a machine learning interatomic potential (MLIP) for predicting properties of doped molybdenum disulfide (MoS2). The validated MLIP enables efficient simulations for discovering new MoS2 materials with tailored performance.

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Published on: January 19, 2018

Area of Science:

Materials Science
Computational Chemistry
Nanotechnology

Background:

Tuning molybdenum disulfide (MoS2) performance requires understanding dopant effects.
Current molecular dynamics simulations for doped MoS2 are limited by a lack of accurate interatomic potentials.

Purpose of the Study:

To evaluate the accuracy of a universal machine learning interatomic potential (MLIP) for 25 different MoS2 dopants.
To establish a computational workflow for designing doped MoS2 materials using MLIPs.

Main Methods:

Benchmarking MLIP-predicted formation energies and structural changes against density functional theory (DFT) calculations for 25 MoS2 dopants.
Performing heating-cooling simulations using the validated MLIP on MoS2 supercells.

Main Results:

The universal MLIP accurately predicts formation energies and structural changes for various MoS2 dopants.
MLIP simulations capture complex phenomena like dopant clustering and MoS2 fracturing at significantly reduced computational cost compared to DFT.
The study provides a validated computational framework for high-throughput screening of doped MoS2.

Conclusions:

Universal MLIPs are a viable and efficient tool for discovering and designing doped MoS2 materials.
This work enables accelerated optimization of MoS2 for tribological, electronic, and optoelectronic applications.