First-principles investigation of the significant anisotropy and ultrahigh thermoelectric efficiency of a novel two-dimensional Ga_(2)I_(2)S_(2) at room temperature

Two-dimensional(2D)thermoelectric(TE)materials have been widely developed;however,some 2D materials exhibit isotropic phonon,electron transport properties,and poor TE performance,which limit their application scope.Thus,exploring excellent anisotropic and ultrahigh-performance TE materials are very warranted.Herein,we first investigate the phonon thermal and TE properties of a novel 2D-connectivity ternary compound named Ga2I2S2.This paper comprehensively studies the phonon dispersion,phonon anharmonicity,lattice thermal conductivity,electronic structure,carrier mobility,Seebeck coefficient,electrical conductivity,and the dimensionless figure of merit(ZT)versus carrier concentration for 2D Ga_(2)I_(2)S_(2).We conclude that the in-plane lattice thermal conductivities of Ga_(2)I_(2)S_(2) at room temperature(300 K)are found to be 1.55 W mK^(−1) in the X-axis direction(xx-direction)and 3.82 W mK^(−1)in the Y-axis direction(yy-direction),which means its anisotropy ratio reaches 1.46.Simultaneously,the TE performance of p-type and n-type doping 2D Ga2I2S2 also shows significant anisotropy,giving rise to the ZT peak values of p-type doping in xx-and yy-directions being 0.81 and 1.99,respectively,and those of n-type doping reach ultrahigh values of 7.12 and 2.89 at 300 K,which are obviously higher than the reported values for p-type and n-type doping ternary compound Sn2BiX(ZT∼1.70 and∼2.45 at 300 K)(2020 Nano Energy 67104283).This work demonstrates that 2D Ga_(2)I_(2)S_(2) has high anisotropic TE conversion efficiency and can also be used as a new potential room-temperature TE material.

First-principles analysis of phonon thermal transport properties of two-dimensional WS2/WSe2 heterostructures

The van der Waals(vdW)heterostructures of bilayer transition metal dichalcogenide obtained by vertically stacking have drawn increasing attention for their enormous potential applications in semiconductors and insulators.Here,by using the first-principles calculations and the phonon Boltzmann transport equation(BTE),we studied the phonon transport properties of WS2/WSe2 bilayer heterostructures(WS2/WSe2-BHs).The lattice thermal conductivity of the ideal WS2/WSe2-BHs crystals at room temperature(RT)was 62.98 W/mK,which was clearly lower than the average lattice thermal conductivity of WS2 and WSe2 single layers.Another interesting finding is that the optical branches below 4.73 THz and acoustic branches have powerful coupling,mainly dominating the lattice thermal conductivity.Further,we also noticed that the phonon mean free path(MFP)of the WS2/WSe2-BHs(233 nm)was remarkably attenuated by the free-standing monolayer WS2(526 nm)and WSe2(1720 nm),leading to a small significant size effect of the WS2/WSe2-BHs.Our results systematically demonstrate the low optical and acoustic phonon modes-dominated phonon thermal transport in heterostructures and give a few important guidelines for the synthesis of van der Waals heterostructures with excellent phonon transport properties.

The role of machine learning in carbon neutrality:Catalyst property prediction,design,and synthesis for carbon dioxide reduction

Achieving carbon neutrality is an essential part of responding to climate change caused by the deforestation and over-exploitation of natural resources that have accompanied the development of human society.The carbon dioxide reduction reaction(CO_(2)RR)is a promising strategy to capture and convert carbon dioxide(CO_(2))into value-added chemical products.However,the traditional trial-and-error method makes it expensive and time-consuming to understand the deeper mechanism behind the reaction,discover novel catalysts with superior performance and lower cost,and determine optimal support structures and electrolytes for the CO_(2)RR.Emerging machine learning(ML)techniques provide an opportunity to integrate material science and artificial intelligence,which would enable chemists to extract the implicit knowledge behind data,be guided by the insights thereby gained,and be freed from performing repetitive experiments.In this perspective article,we focus on recent ad-vancements in ML-participated CO_(2)RR applications.After a brief introduction to ML techniques and the CO_(2)RR,we first focus on ML-accelerated property prediction for potential CO_(2)RR catalysts.Then we explore ML-aided prediction of catalytic activity and selectivity.This is followed by a discussion about ML-guided catalyst and electrode design.Next,the potential application of ML-assisted experimental synthesis for the CO_(2)RR is discussed.

Machine learning accelerated calculation and design of electrocatalysts for CO_(2) reduction

In the past decades,machine learning(ML)has impacted the field of electrocatalysis.Modern researchers have begun to take advantage of ML‐based data‐driven techniques to overcome the computational and experimental limitations to accelerate rational catalyst design.Hence,significant efforts have been made to perform ML to accelerate calculation and aid electrocatalyst design for CO_(2) reduction.This review discusses recent applications of ML to discover,design,and optimize novel electrocatalysts.First,insights into ML aided in accelerating calculation are presented.Then,ML aided in the rational design of the electrocatalyst is introduced,including establishing a data set/data source selection and validation of descriptor selection of ML algorithms validation and predictions of the model.Finally,the opportunities and future challenges are summarized for the future design of electrocatalyst for CO_(2) reduction with the assistance of ML.