Metal alloy anode materials with high specific capacity and low voltage have recently gained significant attention due to their excellent electrochemical performance and the ability to suppress dendrite growth.However...Metal alloy anode materials with high specific capacity and low voltage have recently gained significant attention due to their excellent electrochemical performance and the ability to suppress dendrite growth.However,experimental investigations of metal alloys can be time-consuming and expensive,often requiring extensive experimental design and effort.In this study,we developed a machine learning model based on the Crystal Graph Convolutional Neural Network(CGCNN)to screen alloy anode materials for seven battery systems,including lithium(Li),sodium(Na),potassium(K),zinc(Zn),magnesium(Mg),calcium(Ca),and aluminum(Al).We utilized data with tens of thousands of alloy materials from the Materials Project(MP)and Automatic FLOW for Materials Discovery(AFLOW)databases.Without any experimental voltage input,we identified over 30 alloy systems that have been experimentally validated with good precision.Additionally,we predicted over 100 alloy anodes with low potential and high specific capacity.We hope this work to spur further interest in employing advanced machine learning models for the design of battery materials.展开更多
Accurately predicting the spin Hall conductivity(SHC)is crucial for designing novel spintronic devices that leverage the spin Hall effect.First-principles calculations of SHCs are computationally intensive and unsuita...Accurately predicting the spin Hall conductivity(SHC)is crucial for designing novel spintronic devices that leverage the spin Hall effect.First-principles calculations of SHCs are computationally intensive and unsuitable for quick highthroughput screening.Here,we have developed a residual crystal graph convolutional neural network(Res-CGCNN)deep learning model to classify and predict SHCs solely based on the structural and compositional information.This is enabled by having access to 9249 instances of SHCs data and incorporating extra residual networks into the standard CGCNN framework.We found that Res-CGCNN surpasses CGCNN,achieving a mean absolute error of 115.4(ℏ/e)(S/cm)for regression and an area under the receiver operating characteristic curve of 0.86 for classification.Additionally,we utilized Res-CGCNN to conduct highthroughput screenings of materials in the Materials Project database that were absent in the training set.This led to the prediction of several previously unreported materials displaying large SHCs exceeding 1000(ℏ/e)(S/cm),which were validated through first-principles calculations.This study represents the inaugural endeavor to construct a machine learning model capable of effectively capturing the intricate nonlinear relationship between SHCs and crystal structure and composition,serving as a useful tool for the efficient screening and design of materials exhibiting high SHCs.展开更多
In recent years,interdisciplinary research has become increasingly popular within the scientific community.The fields of materials science and chemistry have also gradually begun to apply the machine learning technolo...In recent years,interdisciplinary research has become increasingly popular within the scientific community.The fields of materials science and chemistry have also gradually begun to apply the machine learning technology developed by scientists from computer science.Graph neural networks(GNNs)are new machine learning models with powerful feature extraction,relationship inference,and compositional generalization capabilities.These advantages drive researchers to design computational models to accelerate material property prediction and new materials design,dramatically reducing the cost of traditional experimental methods.This review focuses on the principles and applications of the GNNs.The basic concepts and advantages of the GNNs are first introduced and compared to the traditional machine learning and neural networks.Then,the principles and highlights of seven classic GNN models,namely crystal graph convolutional neural networks,iCGCNN,Orbital Graph Convolutional Neural Network,MatErials Graph Network,Global Attention mechanism with Graph Neural Network,Atomistic Line Graph Neural Network,and BonDNet are discussed.Their connections and differences are also summarized.Finally,insights and prospects are provided for the rapid development of GNNs in materials science at the atomic scale.展开更多
Mechanical properties consisting of the bulk modulus,shear modulus,Young’s modulus,Poisson’s ratio,etc.,are key factors in determining the practical applications of MAX phases.These mechanical properties are mainly ...Mechanical properties consisting of the bulk modulus,shear modulus,Young’s modulus,Poisson’s ratio,etc.,are key factors in determining the practical applications of MAX phases.These mechanical properties are mainly dependent on the strength of M–X and M–A bonds.In this study,a novel strategy based on the crystal graph convolution neural network(CGCNN)model has been successfully employed to tune these mechanical properties of Ti_(3)AlC_(2)-based MAX phases via the A-site substitution(Ti_(3)(Al1-xAx)C_(2)).The structure–property correlation between the A-site substitution and mechanical properties of Ti_(3)(Al1-xAx)C_(2)is established.The results show that the thermodynamic stability of Ti_(3)(Al1-xAx)C_(2)is enhanced with substitutions A=Ga,Si,Sn,Ge,Te,As,or Sb.The stiffness of Ti_(3)AlC_(2)increases with the substitution concentration of Si or As increasing,and the higher thermal shock resistance is closely associated with the substitution of Sn or Te.In addition,the plasticity of Ti_(3)AlC_(2)can be greatly improved when As,Sn,or Ge is used as a substitution.The findings and understandings demonstrated herein can provide universal guidance for the individual synthesis of high-performance MAX phases for various applications.展开更多
基金supported by the National Key R&D Program of China(2023YFB2504000,YH)a start-up grant from Zhejiang University and the Fundamental Research Funds for the Central Universities(2021FZZX001,226-2024-00005)supported by Special Support Plan for High Level Talents in Zhejiang Province(2023R5231)。
文摘Metal alloy anode materials with high specific capacity and low voltage have recently gained significant attention due to their excellent electrochemical performance and the ability to suppress dendrite growth.However,experimental investigations of metal alloys can be time-consuming and expensive,often requiring extensive experimental design and effort.In this study,we developed a machine learning model based on the Crystal Graph Convolutional Neural Network(CGCNN)to screen alloy anode materials for seven battery systems,including lithium(Li),sodium(Na),potassium(K),zinc(Zn),magnesium(Mg),calcium(Ca),and aluminum(Al).We utilized data with tens of thousands of alloy materials from the Materials Project(MP)and Automatic FLOW for Materials Discovery(AFLOW)databases.Without any experimental voltage input,we identified over 30 alloy systems that have been experimentally validated with good precision.Additionally,we predicted over 100 alloy anodes with low potential and high specific capacity.We hope this work to spur further interest in employing advanced machine learning models for the design of battery materials.
基金supported by the National Natural Science Foundation of China(Grant Nos.52422112,52188101,and 52201030)the National Key R&D Program of China 2021YFB3501503+2 种基金the Strategic Priority Research Program of the Chinese Academy of Sciences(XDA041040402)the Liaoning Province Science and Technology Planning Project(2023021207-JH26/103 and RC230958)the numerical calculations in this study were carried out on the ORISE Supercomputer(Grant No.DFZX202319).
文摘Accurately predicting the spin Hall conductivity(SHC)is crucial for designing novel spintronic devices that leverage the spin Hall effect.First-principles calculations of SHCs are computationally intensive and unsuitable for quick highthroughput screening.Here,we have developed a residual crystal graph convolutional neural network(Res-CGCNN)deep learning model to classify and predict SHCs solely based on the structural and compositional information.This is enabled by having access to 9249 instances of SHCs data and incorporating extra residual networks into the standard CGCNN framework.We found that Res-CGCNN surpasses CGCNN,achieving a mean absolute error of 115.4(ℏ/e)(S/cm)for regression and an area under the receiver operating characteristic curve of 0.86 for classification.Additionally,we utilized Res-CGCNN to conduct highthroughput screenings of materials in the Materials Project database that were absent in the training set.This led to the prediction of several previously unreported materials displaying large SHCs exceeding 1000(ℏ/e)(S/cm),which were validated through first-principles calculations.This study represents the inaugural endeavor to construct a machine learning model capable of effectively capturing the intricate nonlinear relationship between SHCs and crystal structure and composition,serving as a useful tool for the efficient screening and design of materials exhibiting high SHCs.
文摘In recent years,interdisciplinary research has become increasingly popular within the scientific community.The fields of materials science and chemistry have also gradually begun to apply the machine learning technology developed by scientists from computer science.Graph neural networks(GNNs)are new machine learning models with powerful feature extraction,relationship inference,and compositional generalization capabilities.These advantages drive researchers to design computational models to accelerate material property prediction and new materials design,dramatically reducing the cost of traditional experimental methods.This review focuses on the principles and applications of the GNNs.The basic concepts and advantages of the GNNs are first introduced and compared to the traditional machine learning and neural networks.Then,the principles and highlights of seven classic GNN models,namely crystal graph convolutional neural networks,iCGCNN,Orbital Graph Convolutional Neural Network,MatErials Graph Network,Global Attention mechanism with Graph Neural Network,Atomistic Line Graph Neural Network,and BonDNet are discussed.Their connections and differences are also summarized.Finally,insights and prospects are provided for the rapid development of GNNs in materials science at the atomic scale.
基金This work was supported by the National Science Fund for Distinguished Young Scholars(No.52025041)the National Natural Science Foundation of China(Nos.51904021,51974021,and 52174294)the National Key R&D Program of China(No.2021YFB3700400).
文摘Mechanical properties consisting of the bulk modulus,shear modulus,Young’s modulus,Poisson’s ratio,etc.,are key factors in determining the practical applications of MAX phases.These mechanical properties are mainly dependent on the strength of M–X and M–A bonds.In this study,a novel strategy based on the crystal graph convolution neural network(CGCNN)model has been successfully employed to tune these mechanical properties of Ti_(3)AlC_(2)-based MAX phases via the A-site substitution(Ti_(3)(Al1-xAx)C_(2)).The structure–property correlation between the A-site substitution and mechanical properties of Ti_(3)(Al1-xAx)C_(2)is established.The results show that the thermodynamic stability of Ti_(3)(Al1-xAx)C_(2)is enhanced with substitutions A=Ga,Si,Sn,Ge,Te,As,or Sb.The stiffness of Ti_(3)AlC_(2)increases with the substitution concentration of Si or As increasing,and the higher thermal shock resistance is closely associated with the substitution of Sn or Te.In addition,the plasticity of Ti_(3)AlC_(2)can be greatly improved when As,Sn,or Ge is used as a substitution.The findings and understandings demonstrated herein can provide universal guidance for the individual synthesis of high-performance MAX phases for various applications.
