Machine Learning Enabled Prediction of High Stiffness 2D Materials

Nadella, Hema Rajesh; Mukherjee, Sankha; Anand, Abu; Singh, Chandra Veer

doi:10.1021/acsmaterialslett.3c01322

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2024

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“…Machine learning, by learning patterns from extensive MD simulation data, can establish models to predict material performance without the need for expensive computations [17][18][19][20]. For instance, machine learning algorithms can be employed to train on a large dataset generated from MD simulations, and subsequently, the trained model can be utilized to predict material performance under different conditions.…”

Section: Introductionmentioning

confidence: 99%

Machine learning accelerated the prediction of mechanical properties of copper modified by TMDs based on molecular dynamics simulation

Wang,

Gao,

Zhao

et al. 2024

Phys. Scr.

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In this study, we constructed a dataset of elastic modulus and ultimate stress for copper material enhanced by Transition Metal Dichalcogenides (TMDs) through Molecular Dynamics (MD) simulations. Subsequently, leveraging chemical insights, we selected appropriate descriptors and established machine learning prediction models for elastic modulus and ultimate stress, respectively. Finally, the performance of the machine learning models was evaluated using a test set. The results demonstrate excellent performance of the machine learning models in predicting material properties. This work presents a novel approach for efficient material screening, demonstrating the synergy between MD simulations and machine learning in advancing materials research and intelligent material selection platforms.

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Section: Introductionmentioning

confidence: 99%