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Machine learning predicted inelasticity in defective two-dimensional transition metal dichalcogenides using SHAP

Ankit Anuragi1, Ankit Das1, Akash Baski1

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Machine learning models predict mechanical properties of 2D transition metal dichalcogenides (TMDCs) with defects. This research offers insights into tuning material properties for applications while addressing mechanical reliability concerns.

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Area of Science:

  • Materials Science
  • Computational Materials Science
  • Nanotechnology

Background:

  • Crystallographic defects in 2D transition metal dichalcogenides (TMDCs) offer property tuning but can degrade mechanical reliability.
  • Understanding the impact of defects, particularly vacancies, on mechanical properties is crucial for reliable applications.
  • Machine learning (ML) shows promise for modeling materials and uncovering structure-property relationships, though it's an emerging field.

Purpose of the Study:

  • To analyze the mechanical properties of pristine and defected 2D TMDCs using ML and deep learning.
  • To predict key mechanical indicators like failure stress and strain to failure.
  • To investigate the influence of chirality and strain on the mechanical behavior of 2D TMDCs.

Main Methods:

  • Extensive molecular dynamics simulations were conducted on 2D TMDCs with various crystallographic defects.
  • XGBoost and densely connected neural network (DenseNet) algorithms were employed for predictive modeling.
  • Shapley value analysis was used to enhance the interpretability of the ML models.

Main Results:

  • Accurate, state-of-the-art predictions of mechanical properties were achieved using both XGBoost and DenseNet models.
  • The study successfully correlated material structure, including defects and chirality, with mechanical performance.
  • Comparative evaluation highlighted the predictive capabilities and interpretability of the employed ML techniques.

Conclusions:

  • ML and deep learning are effective tools for analyzing the mechanical properties of defected 2D TMDCs.
  • The findings provide a pathway for tuning material properties by controlling defects for specific applications.
  • Improved model interpretability through Shapley values aids in understanding structure-property relationships for reliable material design.