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RESEARCH PAPER · ACS Applied Nano Materials · 2022

Data-Driven Machine Learning to Predict Mechanical Properties of Monolayer Transition-Metal Dichalcogenides for Applications in Flexible Electronics

Prottay Malakar, Md Shajedul Hoque Thakur, Shahriar Muhammad Nahid, Md Mahbubul Islam

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42 direct citing works

Data-Driven Machine Learning to Predict Mechanical Properties of Monolayer Transition-Metal Dichalcogenides for Applications in Flexible ElectronicsOriginalPolarization Modulation on Charge Transfer and Band Structures of GaN/MoS2 Polar Heterojunctions6Designing architected materials for mechanical compression via simulation, deep learning, and experimentation56Machine Learning-Based Prediction of Mechanical Properties and Performance of Nickel–Graphene Nanocomposites Using Molecular Dynamics Simulation Data45Internet of Things and Artificial Intelligence-Integrated Nano-Materials Research1Deviatoric stress-strain curve construction with strain-softening account via the damage-modified Duncan-Chang, arctangent and informer models: a comparative analysis2Dynamic mechanical response prediction model of honeycomb structure based on machine learning method and finite element method331T’- RuWTe2 hybrid monolayer as a novel magnetic material: A first principles study11Horseshoe Lattice Property-Structure Inverse Design Based on Deep Learning4A Molecular Dynamics Study of Mechanical Properties of Vertically Stacked Silicene/MoS2 Van der Waals Heterostructure2Enhancing the stretchability of two-dimensional materials through kirigami: a molecular dynamics study on tungsten disulfide4Machine learning predicted inelasticity in defective two-dimensional transition metal dichalcogenides using SHAP analysis4When Machine Learning Meets 2D Materials: A Review156Study of Structural, Optical and Electrical properties of Zn- doped MoS 2 Nanomaterials for their Supercapacitor Applications0Liquid Phase Exfoliation of 2D Materials and Its Electrochemical Applications in the Data-Driven Future131Explainable chemical artificial intelligence from accurate machine learning of real-space chemical descriptors85Engenharia 4.0: a era da produção inteligente - Volume 130Multiscale computational modeling techniques in study and design of 2D materials: recent advances, challenges, and opportunities25Effect of triangular pits on the mechanical behavior of 2D MoTe2: a molecular dynamics study2Machine learning-driven breakthroughs in water electrolysis and supercapacitors17Synergizing Nanotechnology and Artificial Intelligence for Society 5.0 Advancement Through Intelligent Systems11Applications of machine learning in surfaces and interfaces30Prediction of crystalline structure evolution during solidification of aluminum at different cooling rates using a hybrid neural network model4Predicting the heat of formation and energy above convex hull of 2D MXenes using machine-learning methods6Ti3C2Tx MXene-Based Hybrid Photocatalysts in Organic Dye Degradation: A Review39Recent advances in machine learning guided mechanical properties prediction and design of two-dimensional materials16Recent progress in the design and application of machine learning for the hydrogen evolution reaction in electrocatalysis and photocatalysis3Prediction of the mechanical properties of graphene/copper nanocomposites using machine learning and molecular dynamics simulations4Predicting nanoscale stress-strain curves: A Gaussian processes within a Bayesian framework4Probabilistic prediction of stress–strain curves at the nanoscale: A multi-output Gaussian process approach with molecular dynamics simulations3Facile Discovery of Pristine MoS2 Magnetic Ordering via Machine Learning Prediction and DFT Validation0The Future of MXene Fibers49Carbon-based transition metal chalcogenides for wearable sensors, energy storage, and photodetector applications2Artificial Intelligence-Assisted Nanosensors for Clinical Diagnostics: Current Advances and Future Prospects41Machine learning-based prediction of ultrafast laser ablation in nickel using early-stage transient density evolution0Temperature dependent strength prediction of nanotwinned nickel-based single crystal superalloys by integrating atomistic simulation and machine learning2Defect‐Elimination Strategies for Fabricating High‐Strength and Highly Conductive MXene Fibers62D Nanomaterials Toward Function‐Ready Superlubricity in Advanced Microsystems5Application of Machine Learning in Predicting the Properties of Two-Dimensional Semiconductor Materials3

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  3. Machine Learning-Based Prediction of Mechanical Properties and Performance of Nickel–Graphene Nanocomposites Using Molecular Dynamics Simulation Data

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  5. Deviatoric stress-strain curve construction with strain-softening account via the damage-modified Duncan-Chang, arctangent and informer models: a comparative analysis

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  7. 1T’- RuWTe2 hybrid monolayer as a novel magnetic material: A first principles study

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    Kashmira Dey, S. Shahriar, Md Al Rifat Anan, Prottay Malakar, M. M. Rahman, Mokter Mahmud Chowdhury

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  13. Study of Structural, Optical and Electrical properties of Zn- doped MoS 2 Nanomaterials for their Supercapacitor Applications

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  14. Liquid Phase Exfoliation of 2D Materials and Its Electrochemical Applications in the Data-Driven Future

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  15. Explainable chemical artificial intelligence from accurate machine learning of real-space chemical descriptors

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  16. Engenharia 4.0: a era da produção inteligente - Volume 13

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  18. Effect of triangular pits on the mechanical behavior of 2D MoTe2: a molecular dynamics study

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  19. Machine learning-driven breakthroughs in water electrolysis and supercapacitors

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  24. Ti3C2Tx MXene-Based Hybrid Photocatalysts in Organic Dye Degradation: A Review

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