Single-layer metal dichalcogenides are two-dimensional semiconductors that present strong potential for electronic and sensing applications complementary to that of graphene.

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Electronics and optoelectronics of two-dimensional transition metal dichalcogenides.

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Graphene’s success has shown that it is possible to create stable, single and few-atom-thick layers of van der Waals materials, and also that these materials can exhibit fascinating and technologically useful properties. Here we review the state-of-the-art of 2D materials beyond graphene. Initially, we will outline the different chemical classes of 2D materials and discuss the various strategies to prepare single-layer, few-layer, and multilayer assembly materials in solution, on substrates, and on the wafer scale. Additionally, we present an experimental guide for identifying and characterizing single-layer-thick materials, as well as outlining emerging techniques that yield both local and global information. We describe the differences that occur in the electronic structure between the bulk and the single layer and discuss various methods of tuning their electronic properties by manipulating the surface. Finally, we highlight the properties and advantages of single-, few-, and many-layer 2D materials in...

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Progress, Challenges, and Opportunities in Two-Dimensional Materials Beyond Graphene

Preceding the current interest in layered materials for electronic applications, research in the 1960's found that black phosphorus combines high carrier mobility with a fundamental band gap. We introduce its counterpart, dubbed few-layer phosphorene, as a new 2D p-type material. Same as graphene and MoS2, phosphorene is flexible and can be mechanically exfoliated. We find phosphorene to be stable and, unlike graphene, to have an inherent, direct and appreciable band-gap that depends on the number of layers. Our transport studies indicate a carrier mobility that reflects its structural anisotropy and is superior to MoS2. At room temperature, our phosphorene field-effect transistors with 1.0 um channel length display a high on-current of 194 mA/mm, a high hole field-effect mobility of 286 cm2/Vs, and an on/off ratio up to 1E4. We demonstrate the possibility of phosphorene integration by constructing the first 2D CMOS inverter of phosphorene PMOS and MoS2 NMOS transistors.

Phosphorene: A New 2D Material with High Carrier Mobility

The lithium metal battery is strongly considered to be one of the most promising candidates for high-energy-density energy storage devices in our modern and technology-based society. However, uncontrollable lithium dendrite growth induces poor cycling efficiency and severe safety concerns, dragging lithium metal batteries out of practical applications. This review presents a comprehensive overview of the lithium metal anode and its dendritic lithium growth. First, the working principles and technical challenges of a lithium metal anode are underscored. Specific attention is paid to the mechanistic understandings and quantitative models for solid electrolyte interphase (SEI) formation, lithium dendrite nucleation, and growth. On the basis of previous theoretical understanding and analysis, recently proposed strategies to suppress dendrite growth of lithium metal anode and some other metal anodes are reviewed. A section dedicated to the potential of full-cell lithium metal batteries for practical applicatio...

Toward Safe Lithium Metal Anode in Rechargeable Batteries: A Review.

Identifying Chemical Factors Affecting Reaction Kinetics in Li-air Battery via ab initio Calculations and Machine Learning

Despite the usage of both experimental and topological methods, realizing a rapid and accurate measurement of the onset temperature (Tg) of GexSe1−x glass transition remains an open challenge. In this paper, a predictive model for the Tg in GexSe1−x glass system is presented by a machine learning method named feature selection based two-stage support vector regression (FSTS-SVR). Firstly, Pearson correlation coefficient (PCC) is used to select features highly correlated with Tg from the candidate features of GexSe1−x glass system. Secondly, in order to simulate the two-stage characteristic of Tg which is caused by structural variation with a turning point at x = 0.33 via the structural analysis, SVR is utilized to build predictive models for two stages separately and then the two achieved models are synthesized using a minimum error based model for Tg prediction. Compared with the topological and other methods based on SVR, the FSTS-SVR gives the highest predictive accuracy with the root mean square error (RMSE) and mean absolute percentage error (MAPE) of 10.64 K and 2.38%, respectively. This method is also expected to be more efficient for the prediction of Tg of other glass systems with the multi-stage characteristic.

Predicting the onset temperature (Tg) of GexSe1−x glass transition: a feature selection based two-stage support vector regression method

Rotational motion of polyanion versus volume effect associated with ionic conductivity of several solid electrolytes

Rational design of solid-state electrolytes (SSEs) with high ionic conductivity and low activation energy (Ea) is vital for all solid-state batteries. Machine learning (ML) techniques have recently been successful in predicting Li+ conduction property in SSEs with various descriptors and accelerating the development of SSEs. In this work, we extend the previous efforts and introduce a framework of ML prediction for Ea in SSEs with hierarchically encoding crystal structure-based (HECS) descriptors. Taking cubic Li-argyrodites as an example, an Ea prediction model is developed to the coefficient of determination (R2) and root-mean-square error (RMSE) values of 0.887 and 0.02 eV for training dataset, and 0.820 and 0.02 eV for test dataset, respectively by partial least squares (PLS) analysis, proving the prediction power of HECS-descriptors. The variable importance in projection (VIP) scores demonstrate the combined effects of the global and local Li+ conduction environments, especially the anion size and the resultant structural changes associated with anion site disorder. The developed Ea prediction model directs us to optimize and design new Li-argyrodites with lower Ea, such as Li6–xPS5–xCl1+x (

Siqi Shi

Papers

Identifying Chemical Factors Affecting Reaction Kinetics in Li-air Battery via ab initio Calculations and Machine Learning

Predicting the onset temperature (Tg) of GexSe1−x glass transition: a feature selection based two-stage support vector regression method

Rotational motion of polyanion versus volume effect associated with ionic conductivity of several solid electrolytes

Machine learning prediction of activation energy in cubic Li-argyrodites with hierarchically encoding crystal structure-based (HECS) descriptors

Ab initio investigation of the surface properties of Cu(111) and Li diffusion in Cu thin film