Predicting mechanical properties of defective h-BN nanosheets using Data-Driven models

SA Mousavi and A Montazeri, COMPUTATIONAL MATERIALS SCIENCE, 228, 112380 (2023).

DOI: 10.1016/j.commatsci.2023.112380

Machine learning (ML) methods have assisted different fields of science in overcoming challenging problems. In this work, with the aid of ML and deep learning algorithms, predicting the mechanical properties of defective single-layer hexagonal boron nitride (h-BN) nanosheets is accelerated compared to conventional atomistic simulations. To provide the required data for ML models, molecular dynamic (MD) simulation is used to determine the Young modulus, ultimate tensile strength, and fracture strain of these 2D nanostructures under the influence of various defect features, namely density, type, structure, and distribution. The obtained MD results exhibit strong correlation between the mechanical properties of defective h-BN monolayers and the introduced defect characteristics. Accuracy of the results is supported by the analysis of variance (ANOVA) and correlation coefficient between independent and dependent variables. In the next step, three ML algorithms including SVR, Random Forest and XGBoost, and three artificial neural network (ANN) models with different hidden layers are trained to accomplish our goals. Among all the developed models, an ANN design with four hidden layers performs better with the highest R2 score of 0.86. Our findings provide significant assets for defect engineering by accelerating the mechanical properties prediction of defective h-BN monolayers compared to traditional MD simulations.

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