Metal oxides are widely used in the fields of chemistry, physics and materials. Oxygen vacancy formation energy is a key parameter to describe the chemical, mechanical, and thermodynamic properties of...

Two-dimensional (2D) hybrid organic-inorganic perovskites (HOIPs) have attracted considerable interest for their promising applications for solar cells and optoelectronics. However, the fast and accurate prediction of basic band structure of...

Exfoliation energy is one of the fundamental parameters in the science and engineering of two-dimensional (2D) materials. Traditionally, it was obtained via indirect experimental measurement or first-principles calculations, which are very time-and resource-consuming. Herein, we provide an efficient machine learning (ML) method to accurately predict the exfoliation energies for 2D materials. Toward this end, a series of simple descriptors with explicit physical meanings are defined. Regression trees (RT), support vector machines (SVM), multiple linear regression (MLR), and ensemble trees (ET) are compared to develop the most suitable model for the prediction of exfoliation energies. It is shown that the ET model can efficiently predict the exfoliation energies through extensive validations and stability analysis. The influence of the defined features on the exfoliation energies is analyzed by sensitivity analysis to provide novel physical insight into the affecting factors of the exfoliation energies.

This work investigates possible improvements in the accuracy of semiempirical quantum chemistry (SQC) methods for the prediction of standard enthalpy of formation (Δ f H o) through the use of an artificial neural network (ANN) with molecular descriptors. A total of 142 organic compounds with enough structural diversity has been considered in the training set. Standard enthalpy of formation for the selected compounds at the semiempirical PM3 and PM6 quantum chemistry methods is collected from literature and is calculated by using the semiempirical PM7 method in this work. The multiple stepwise regression is first used to screen effective molecular descriptors, which are highly correlated with the error terms of the standard enthalpy of formation compared with experimental values. The obtained seven effective molecular descriptors are then used as input set to establish three 7-11-1 neural network-based correction models to improve the accuracy of SQC methods. By using the developed correction models, the mean absolute errors for Δ f H o of PM3, PM6, and PM7 methods are reduced from 22.36, 18.60, and 17.27 to 9.86, 9.83, and 8.95, respectively, in kJ/mol. Meanwhile, the results of the test set show that the neural network does not have the problem of overfitting. Detailed analysis of the seven effective molecular descriptors indicates that the major source of the correction models is the electron-withdrawing effect. The developed ANN models for the three selected SQC methods provide an efficient method for the quick and accurate prediction of thermodynamic properties. K E Y W O R D S artificial neural network, enthalpy of formation, molecular descriptors, semiempirical quantum chemistry methods 1 | INTRODUCTION Thermodynamic properties are critical in almost all branches of chemistry and chemical engineering, such as chemical kinetics, combustion processing, and petroleum industry. [1,2] They are used to determine heat of reaction, equilibrium constant, reaction rate constants, and so on. Thus, accurate prediction of thermodynamic properties is one of the major focuses for both experimental and theoretical researchers. [3,4] Among the various thermodynamic properties, standard enthalpy of formation (Δ f H o) is a key parameter of species. Generally, experimental measurements of thermodynamic properties are reliable but become difficult and expensive as the research systems become larger. Hence, empirical methods were developed. Among the empirical methods, the group additivity (GA) method developed by Benson et al [5] is one of the quickest

No abstract

While the thermoelectric (TE) materials have attracted significant attention in recent years, the design and discovery of new TE materials with optimal carrier concentration and band gap remains a great challenge. Herein, we report the development of machine learning (ML) methods to predict TE materials with introducing physically meaningful simple descriptors. Specifically, we use the number of electrons, Pauling electronegativity and relative atomic mass as the basic physical variables and compute 242 descriptors in 64 categories to characterize the molecular information of a TE material. Multiple stepwise regression is employed to reduce the dimensionality in the developed ML models, and 5 and 4 important features for the band gap and carrier concentration is selected, respectively. The important features are used as input of a total number of 19 ML methods to select the optimal ML models for the prediction of band gap and carrier concentration, respectively. It is shown that the least square support vector machines method is the best model for the prediction of the band gap, while the back propagating artificial neutral network model exhibits the best performance in predicting the carrier concentration values. This work provides novel theoretical guidance for the rapid prediction properties of TE materials. The simple descriptors we defined can accurately predict the band gap and carrier concentration of quaternary TE materials.

Recently, electrides have received increasing attention due to their multifunctional properties as superconducting, catalytic, insulating, and electrode materials, with potential to offer other performance and possess novel physical states. This work uncovers that Li5N as an electride possess four novel physical states simultaneously: electride state, super-coordinated state, superconducting state, and superionic state. By obtaining high-pressure phase diagrams of the Li–N system at 150–350 GPa using a crystal structure search algorithm, we find that Li5N can remain stable as P6/mmm structure and has a 14-fold super-coordination number, as verified by Bader charge and electron localization function analysis. Its superconducting transition temperature reaches the highest at 150 GPa (T c = 48.97 K). Besides, Li5N exhibits the superionic state at 3000 K, in which N atoms act like solid, while some Li atoms flow like liquid. The above results are further verified at a macroscopic level by using deep learning potential molecular dynamics simulations.

No abstract