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physics-embedded machine learning search for Sm-doped PMN-PT piezoelectric ceramics with high performance

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Chinese physics B 2024年第8期33卷 81-88页

作者：辛睿王亚祺房泽郑凤基高雯付大石史国庆刘建一张永成 College of physics Center for Marine Observation and CommunicationsNational Demonstration Center for Experimental Applied Physics EducationQingdao UniversityQingdao 266071China Centre for Theoretical and computational physics College of PhysicsQingdao UniversityQingdao 266071China

Pb(Mg_(1/3)Nb_(2/3))O_(3)–PbTiO_(3)(PMN-PT)piezoelectric ceramics have excellent piezoelectric properties and are used in a wide range of *** the solid solution ratios of PMN/PT and different concentrations of elemental doping are the main methods to modulate their piezoelectric *** combination of these controllable conditions leads to an exponential increase of possible compositions in ceramics,which makes it not easy to extend the sample data by additional experimental or theoretical *** this paper,a physics-embedded machine learning method is proposed to overcome the difficulties in obtaining piezoelectric coefficients and Curie temperatures of Sm-doped PMN-PT ceramics with different *** contrast to all-data-driven model,physics-embedded machine learning is able to learn nonlinear variation rules based on small datasets through potential correlation between ferroelectric *** on the model outputs,the positions of morphotropic phase boundary(MPB)with different Sm doping amounts are *** also find the components with the best piezoelectric property and comprehensive ***,we set up a database according to the obtained results,through which we can quickly find the optimal components of Sm-doped PMN-PT ceramics according to our specific needs.

关键词： Pb(Mg_(1/3)Nb_(2/3))O_(3)–PbTiO_(3)(PMN-PT)ceramic physics-embedded machine learning piezoelectric coefficient Curie temperature

Integrated separation-electrochemical detection device based on wood column for online identification of enantiomer

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Nano Research 2024年第3期17卷 1893-1901页

作者： Junli Guo Yuanfei Xue Chenxi Zhao Junjian Zhao Zhida Gao Ye Mei Yan-Yan Song College of Sciences Northeastern UniversityShenyang 110004China State Key Laboratory of Precision Spectroscopy School of Physics and Electronic ScienceEast China Normal UniversityShanghai 200062China NYU-ECNU center for computational Chemistry at NYU Shanghai Shanghai 200062China Collaborative innovation center of Extreme Optics Shanxi UniversityTaiyuan 030006China

Chiral chemicals have attracted significant interest in the pharmaceutical industry,yet the separation methods to get pure enantiomers from racemic mixture are still *** date,the separation of enantiomers still mainly depends on chromatography using high-cost chiral stationary ***,wood channels were used as the handheld integrated device,and enantiomer separation was simultaneously detected using an electrochemical *** this method,a chiral UIO-66(L-UIO-66)modified enantiomer separation zone and carbonized wood based online detection zone are integrated along a single wood *** on the in situ separation results from the chronoamperometry data,the wood device shows excellent separation ability for a wide range of electrochemically active enantiomers,including 3,4-dihydroxyphenylalanine,amino acids,ascorbic acid,carnitine,and penicillamine with high chirality *** unbiased molecular dynamic simulations indicate that the excellent chiral recognition and separation are attributed to the different barriers from the bound states to the dissociated state of the enantiomers in the homochiral microenvironment of the *** integrated enantiomer separation-electrochemical detection device provides a novel,easy,and low-cost platform for the separation of pure enantiomer from racemic mixture.

关键词： wood column integrated separation-detection device enantioselectivity online sensing free energy barrier

The CALYPSO methodology for structure prediction

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Chinese physics B 2019年第10期28卷 22-29页

作者： Qunchao Tong Jian Lv Pengyue Gao Yanchao Wang innovation center of computational physics methods and software State Key Laboratory of Superhard Materials College of PhysicsJilin University

Structure prediction methods have been widely used as a state-of-the-art tool for structure searches and materials discovery, leading to many theory-driven breakthroughs on discoveries of new materials. These methods generally involve the exploration of the potential energy surfaces of materials through various structure sampling techniques and optimization algorithms in conjunction with quantum mechanical calculations. By taking advantage of the general feature of materials potential energy surface and swarm-intelligence-based global optimization algorithms, we have developed the CALYPSO method for structure prediction, which has been widely used in fields as diverse as computational physics, chemistry, and materials science. In this review, we provide the basic theory of the CALYPSO method, placing particular emphasis on the principles of its various structure dealing methods. We also survey the current challenges faced by structure prediction methods and include an outlook on the future developments of CALYPSO in the conclusions.

关键词： structure prediction CALYPSO method crystal structure potential energy surface

High-Performance Inverted Perovskite Solar Cells with Sol-Gel-Processed Sliver-Doped NiOx Hole Transporting Layer

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Energy & Environmental Materials 2024年第4期7卷 205-214页

作者： Haibin Wang Zhiyin Qin Xinjian Li Chun Zhao Chao Liang School of Advanced Technology Xi'an Jiaotong-Liverpool University Department of Electrical Engineering and Electronics University of Liverpool School of Humanities and Social Sciences Xi'an Jiaotong-Liverpool University Key Laboratory of Materials physics of Ministry of Education School of Physics and Microelectronics Zhengzhou University MOE Key Laboratory for Nonequilibrium Synthesis and Modulation of Condensed Matter School of Physics National Innovation Platform (Center) for Industry-Education Integration of Energy Storage Technology Xi'an Jiaotong University

Nickel oxide （NiOx） has been established as a highly efficient and stable holetransporting layer （HTL） in perovskite solar cells （PSCs）.However,existing deposition methods for NiOxhave been restricted by high-vacuum processes and fail to address the energy level mismatch at the NiOx/perovskite interface,which has impeded the development of PSCs.

关键词： Ag-NiOX/NiOX hole transporting layer inverted perovskite solar cells

Interface structure prediction via CALYPSO method

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Science Bulletin 2019年第5期64卷 301-309页

作者： Bo Gao Pengyue Gao Shaohua Lu Jian Lv Yanchao Wang Yanming Ma State Key Laboratory of Superhard Materials & innovation center for computational physics methods and softwares College of Physics Jilin University International center of Future Science Jilin University

The atomistic structures of solid–solid interfaces are of fundamental interests for understanding physical properties of interfacial materials. However, determination of interface structures faces a substantial challenge, both experimentally and theoretically. Here, we propose an efficient method for predicting interface structures via the generalization of our in-house developed CALYPSO method for structure prediction. We devised a lattice match toolkit that allows us to automatically search for the optimal latticematched superlattice for construction of the interface structures. In addition, bonding constraints(e.g.,constraints on interatomic distances and coordination numbers of atoms) are imposed to generate better starting interface structures by taking advantages of the known bonding environment derived from the stable bulk phases. The interface structures evolve by following interfacially confined swarm intelligence algorithm, which is known to be efficient for exploration of potential energy surface. The method was validated by correctly predicting a number of known interface structures with only given information of two parent solids. The application of the developed method leads to prediction of two unknown grain boundary(GB) structures(r-GB and p-GB) of rutile TiO_2 ∑5(2 1 0) under an O reducing atmosphere that contained Ti^(3+)as the result of O defects. Further calculations revealed that the intrinsic band gap of p-GB is reduced to 0.7 eV owing to substantial broadening of the Ti-3 d interfacial levels from Ti^(3+)*** results demonstrated that introduction of grain boundaries is an effective strategy to engineer the electronic properties and thus enhance the visible-light photoactivity of TiO_2.

关键词： Solid-solid interface Structure prediction method Lattice mismatch TiO2 grain boundary

Advances of machine learning in materials science: Ideas and techniques

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Frontiers of physics 2024年第1期19卷 155-194页

作者： Sue Sin Chong Yi Sheng Ng Hui-Qiong Wang Jin-Cheng Zheng Department of New Energy Science and Engineering Xiamen University MalaysiaSepang 43900Malaysia Engineering Research center of Micro-nano Optoelectronic Materials and Devices Ministry of EducationFujian Key Laboratory of Semiconductor Materials and ApplicationsCI Center for OSEDand Department of PhysicsXiamen UniversityXiamen 361005China

In this big data era, the use of large dataset in conjunction with machine learning (ML) has been increasingly popular in both industry and academia. In recent times, the field of materials science is also undergoing a big data revolution, with large database and repositories appearing everywhere. Traditionally, materials science is a trial-and-error field, in both the computational and experimental departments. With the advent of machine learning-based techniques, there has been a paradigm shift: materials can now be screened quickly using ML models and even generated based on materials with similar properties;ML has also quietly infiltrated many sub-disciplinary under materials science. However, ML remains relatively new to the field and is expanding its wing quickly. There are a plethora of readily-available big data architectures and abundance of ML models and software;The call to integrate all these elements in a comprehensive research procedure is becoming an important direction of material science research. In this review, we attempt to provide an introduction and reference of ML to materials scientists, covering as much as possible the commonly used methods and applications, and discussing the future possibilities.

关键词： machine learning materials science

Superconductivity in Shear Strained Semiconductors

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Chinese physics Letters 2021年第8期38卷 73-78页

作者： Chang Liu Xianqi Song Quan Li Yanming Ma Changfeng Chen State Key Lab of Superhard Materials and International Center for Computational Methods and SoftwareCollege of PhysicsJilin UniversityChangchun 130012China International center of Future Science Jilin UniversityChangchun 130012China Department of physics and Astronomy University of NevadaLas VegasNevada 89154USA

Semiconductivity and superconductivity are remarkable quantum phenomena that have immense impact on science and technology,and materials that can be tuned,usually by pressure or doping,to host both types of quantum states are of great fundamental and practical *** we show by first-principles calculations a distinct route for tuning semiconductors into superconductors by diverse large-range elastic shear strains,as demonstrated in exemplary cases of silicon and silicon *** of strain driven evolution of bonding structure,electronic states,lattice vibration,and electron-phonon coupling unveils robust pervading deformation induced mechanisms auspicious for modulating semiconducting and superconducting states under versatile material *** finding opens vast untapped structural configurations for rational exploration of tunable emergence and transition of these intricate quantum phenomena in a broad range of materials.

关键词： quantum Shear rational

Predicting the structure and stability of titanium oxide electrides

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npj computational Materials 2018年第1期4卷 63-68页

作者： Xin Zhong Meiling Xu Lili Yang Xin Qu Lihua Yang Miao Zhang Hanyu Liu Yanming Ma Key Laboratory of Functional Materials physics and Chemistry of the Ministry of Education Jilin Normal UniversityChangchun 130103China National Demonstration center for Experimental physics Education Jilin Normal UniversitySiping 136000China Beijing computational Science Research center Beijing 100193China School of physics and Electronic Engineering Jiangsu Normal UniversityXuzhou 221116China Department of physics School of ScienceBeihua UniversityJilin 132013China State Key Lab of Superhard Materials&innovation center for computational physics methods and software College of PhysicsJilin UniversityChangchun 130012China International center for Future Science Jilin UniversityChangchun 130012China

The search for new inorganic electrides has attracted significant attention due to their potential applications in transparent conductors,battery electrodes,electron emitters,as well as catalysts for chemical ***,only a few inorganic electrides have been successfully synthesized thus far,limiting the variety of electride ***,we show the stabilization of inorganic electrides in the Ti-rich Ti–O system through first-principles calculations in conjunction with swarm-intelligence-based CALYPSO method for structure *** the known Ti-rich stoichiometries of Ti_(2)O,Ti_(3)O,and Ti_(6)O,two hitherto unknown Ti_(4)O and Ti_(5)O stoichiometries are predicted to be thermodynamically stable at certain pressure *** found that these Tirich Ti–O compounds are primarily zero-dimensional electrides with excess electrons confined in the atom-sized lattice voids or between the cationic layers playing the role as *** underlying mechanism behind the stabilization of electrides has been rationalized in terms of the excess electrons provided by Ti atoms and their accommodation of excess electrons by multiple cavities and layered atomic *** present results provide a viable direction for searching for practical electrides in the technically important Ti–O system.

关键词： inorganic excess stabilization

ThyroidNet:A Deep Learning Network for Localization and Classification of Thyroid Nodules

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Computer Modeling in Engineering & Sciences 2024年第4期139卷 361-382页

作者： Lu Chen Huaqiang Chen Zhikai Pan Sheng Xu Guangsheng Lai Shuwen Chen Shuihua Wang Xiaodong Gu Yudong Zhang Ultrasonic Department Zhongda Hospital Affiliated to Southeast UniversityNanjing210009China School of physics and Information Engineering Jiangsu Second Normal UniversityNanjing211200China School of Computing and Mathematical Sciences University of LeicesterLeicesterLE17RHUK Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia State Key Laboratory of MillimeterWaves Southeast UniversityNanjing210096China Jiangsu Province Engineering Research center of Basic Education Big Data Application Jiangsu Second Normal UniversityNanjing211200China School of software Engineering Quanzhou Normal UniversityQuanzhou362000China Department of Biological Sciences Xi’an Jiaotong-Liverpool UniversitySuzhou215123China

Aim:This study aims to establish an artificial intelligence model,ThyroidNet,to diagnose thyroid nodules using deep learning techniques ***:A novel method,ThyroidNet,is introduced and evaluated based on deep learning for the localization and classification of thyroid ***,we propose the multitask TransUnet,which combines the TransUnet encoder and decoder with multitask ***,we propose the DualLoss function,tailored to the thyroid nodule localization and classification *** balances the learning of the localization and classification tasks to help improve the model’s generalization ***,we introduce strategies for augmenting the ***,we submit a novel deep learning model,ThyroidNet,to accurately detect thyroid ***:ThyroidNet was evaluated on private datasets and was comparable to other existing methods,including U-Net and *** results show that ThyroidNet outperformed these methods in localizing and classifying thyroid *** achieved improved accuracy of 3.9%and 1.5%,***:ThyroidNet significantly improves the clinical diagnosis of thyroid nodules and supports medical image analysis *** research directions include optimization of the model structure,expansion of the dataset size,reduction of computational complexity and memory requirements,and exploration of additional applications of ThyroidNet in medical image analysis.

关键词： ThyroidNet deep learning TransUnet multitask learning medical image analysis