The stable sub-angstrom resolution of the aberration-corrected scanning transmission electron microscope(ACSTEM)makes it an advanced and practical characterization technique for all materials.Owing to the prosperous a...The stable sub-angstrom resolution of the aberration-corrected scanning transmission electron microscope(ACSTEM)makes it an advanced and practical characterization technique for all materials.Owing to the prosperous advancement in computational technology,specialized software and programs have emerged as potent facilitators across the entirety of electron microscopy characterization process.Utilizing advanced image processing algorithms promotes the rectification of image distortions,concurrently elevating the overall image quality to superior standards.Extracting high-resolution,pixel-level discrete information and converting it into atomic-scale,followed by performing statistical calculations on the physical matters of interest through quantitative analysis,represent an effective strategy to maximize the value of electron microscope images.The efficacious utilization of quantitative analysis of electron microscope images has become a progressively prominent consideration for materials scientists and electron microscopy researchers.This article offers a concise overview of the pivotal procedures in quantitative analysis and summarizes the computational methodologies involved from three perspectives:contrast,lattice and strain,as well as atomic displacements and polarization.It further elaborates on practical applications of these methods in electronic functional materials,notably in piezoelectrics/ferroelectrics and thermoelectrics.It emphasizes the indispensable role of quantitative analysis in fundamental theoretical research,elucidating the structure–property correlations in high-performance systems,and guiding synthesis strategies.展开更多
Thermoelectric and thermal materials are essential in achieving carbon neutrality. However, the high cost of lattice thermal conductivity calculations and the limited applicability of classical physical models have le...Thermoelectric and thermal materials are essential in achieving carbon neutrality. However, the high cost of lattice thermal conductivity calculations and the limited applicability of classical physical models have led to the inefficient development of thermoelectric materials. In this study, we proposed a two-stage machine learning framework with physical interpretability incorporating domain knowledge to calculate high/low thermal conductivity rapidly. Specifically, crystal graph convolutional neural network(CGCNN) is constructed to predict the fundamental physical parameters related to lattice thermal conductivity. Based on the above physical parameters, an interpretable machine learning model–sure independence screening and sparsifying operator(SISSO), is trained to predict the lattice thermal conductivity. We have predicted the lattice thermal conductivity of all available materials in the open quantum materials database(OQMD)(https://www.oqmd.org/). The proposed approach guides the next step of searching for materials with ultra-high or ultralow lattice thermal conductivity and promotes the development of new thermal insulation materials and thermoelectric materials.展开更多
基金Project supported by the financial support from the National Key R&D Program of China(Grant No.2021YFB3201100)the National Natural Science Foundation of China(Grant No.52172128)the Top Young Talents Programme of Xi’an Jiaotong University.
文摘The stable sub-angstrom resolution of the aberration-corrected scanning transmission electron microscope(ACSTEM)makes it an advanced and practical characterization technique for all materials.Owing to the prosperous advancement in computational technology,specialized software and programs have emerged as potent facilitators across the entirety of electron microscopy characterization process.Utilizing advanced image processing algorithms promotes the rectification of image distortions,concurrently elevating the overall image quality to superior standards.Extracting high-resolution,pixel-level discrete information and converting it into atomic-scale,followed by performing statistical calculations on the physical matters of interest through quantitative analysis,represent an effective strategy to maximize the value of electron microscope images.The efficacious utilization of quantitative analysis of electron microscope images has become a progressively prominent consideration for materials scientists and electron microscopy researchers.This article offers a concise overview of the pivotal procedures in quantitative analysis and summarizes the computational methodologies involved from three perspectives:contrast,lattice and strain,as well as atomic displacements and polarization.It further elaborates on practical applications of these methods in electronic functional materials,notably in piezoelectrics/ferroelectrics and thermoelectrics.It emphasizes the indispensable role of quantitative analysis in fundamental theoretical research,elucidating the structure–property correlations in high-performance systems,and guiding synthesis strategies.
基金support of the National Natural Science Foundation of China(Grant Nos.12104356 and52250191)China Postdoctoral Science Foundation(Grant No.2022M712552)+2 种基金the Opening Project of Shanghai Key Laboratory of Special Artificial Microstructure Materials and Technology(Grant No.Ammt2022B-1)the Fundamental Research Funds for the Central Universitiessupport by HPC Platform,Xi’an Jiaotong University。
文摘Thermoelectric and thermal materials are essential in achieving carbon neutrality. However, the high cost of lattice thermal conductivity calculations and the limited applicability of classical physical models have led to the inefficient development of thermoelectric materials. In this study, we proposed a two-stage machine learning framework with physical interpretability incorporating domain knowledge to calculate high/low thermal conductivity rapidly. Specifically, crystal graph convolutional neural network(CGCNN) is constructed to predict the fundamental physical parameters related to lattice thermal conductivity. Based on the above physical parameters, an interpretable machine learning model–sure independence screening and sparsifying operator(SISSO), is trained to predict the lattice thermal conductivity. We have predicted the lattice thermal conductivity of all available materials in the open quantum materials database(OQMD)(https://www.oqmd.org/). The proposed approach guides the next step of searching for materials with ultra-high or ultralow lattice thermal conductivity and promotes the development of new thermal insulation materials and thermoelectric materials.
