Family of Two Dimensional Transition Metal Dichlorides Fundamental Properties, Structural Defects, and Environmental Stability

04/29/2022

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A large number of novel two-dimensional (2D) materials are constantly discovered and deposed into the databases. Consolidate implementation of machine learning algorithms and density functional theory (DFT) based predictions have allowed creating several databases containing an unimaginable amount of 2D samples. The next step in this chain, the investigation leads to a comprehensive study of the functionality of the invented materials. In this work, a family of transition metal dichlorides has been screened out for systematical investigation of their structural stability, fundamental properties, structural defects, and environmental stability via DFT based calculations. The work highlights the importance of using the potential of the invented materials and proposes a comprehensive characterization of a new family of 2D materials.

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Family of Two Dimensional Transition Metal Dichlorides Fundamental Properties, Structural Defects, and Environmental Stability

Data-driven discovery of novel 2D materials by deep generative models

Large-scale machine-learning-assisted exploration of the whole materials space

Interpretable discovery of new semiconductors with machine learning

Materials databases: the need for open, interoperable databases with standardized data and rich metadata

Hierarchical Visualization of Materials Space with Graph Convolutional Neural Networks

Fast and accessible first-principles calculations of vibrational properties of materials

ImageMech: From image to particle spring network for mechanical characterization

Family of Two Dimensional Transition Metal Dichlorides Fundamental Properties, Structural Defects, and Environmental Stability

Related Research

Data-driven discovery of novel 2D materials by deep generative models

Large-scale machine-learning-assisted exploration of the whole materials space

Interpretable discovery of new semiconductors with machine learning

Materials databases: the need for open, interoperable databases with standardized data and rich metadata

Hierarchical Visualization of Materials Space with Graph Convolutional Neural Networks

Fast and accessible first-principles calculations of vibrational properties of materials

ImageMech: From image to particle spring network for mechanical characterization