Application of deep learning for informatics aided design of electrode materials in metal-ion batteries

To develop emerging electrode materials and improve the performances of batteries,the machine learning techniques can provide insights to discover,design and develop battery new materials in high-throughput way.In this paper,two deep learning models are developed and trained with two feature groups extracted from the Materials Project datasets to predict the battery electrochemical performances including average voltage,specific capacity and specific energy.The deep learning models are trained with the multilayer perceptron as the core.The Bayesian optimization and Monte Carlo methods are applied to improve the prediction accuracy of models.Based on 10 types of ion batteries,the correlation coefficients are maintained above 0.9 compared to DFT calculation results and the mean absolute error of the prediction results for voltages of two models can reach 0.41 V and 0.20 V,respectively.The electrochemical performance prediction times for the two trained models on thousands of batteries are only 72.9 ms and 75.7 ms.Besides,the two deep learning models are applied to approach the screening of emerging electrode materials for sodium-ion and potassium-ion batteries.This work can contribute to a high-throughput computational method to accelerate the rational and fast materials discovery and design.

Enabling dendrite-free charging for lithium batteries based on transport-reaction competition mechanism in CHAIN framework

Worldwide trends in mobile electrification will skyrocket demands for lithium-based battery production,driven by the popularity of electric vehicles.However,both lithium metal batteries and lithium ion batteries face severe safety issues due to dendrite nucleation and growth process.Li deposition is significantly influenced by interfacial factors and charging conditions.In this paper,an electrochemical model considering the internal and external factors is proposed based on Monte Carlo method.The influence of internal solid electrolyte interphase(SEI)porosity,thickness and the external conditions on dendrite growth process is systematically described.The simulation results support that the three factors investigated in this model could synergistically regulate the dendrite growth process.Three competition mechanisms are proposed to tailor lithium deposition for Li-based batteries and numerical solutions for variation pattern of dendrite growth with time are fitted.A three-step process describing kinetic process of lithium deposition is proposed.To achieve dendrite-free charging process,charging strategies and emerging materials design should be considered,including physicochemical materials engineering,artificial SEI,and design for dynamic safety boundary.This work could contribute to the foundation for insights of Li deposition mechanism,which is promising to provide guidelines for next-generation high-energy-density and safe batteries in CHAIN framework.

Neuro-11:a new questionnaire for the assessment of somatic symptom disorder in general hospitals

Background Somatic symptom disorder(SSD)commonly presents in general hospital settings,posing challenges for healthcare professionals lacking specialised psychiatric training.The Neuro-11 Neurosis Scale(Neuro-11)offers promise in screening and evaluating psychosomatic symptoms,comprising 11 concise items across three dimensions:somatic symptoms,negative emotions and adverse events.Prior research has validated the scale’s reliability,validity and theoretical framework in somatoform disorders,indicating its potential as a valuable tool for SSD screening in general hospitals.Aims This study aimed to establish the reliability,validity and threshold of the Neuro-11 by comparing it with standard questionnaires commonly used in general hospitals for assessing SSD.Through this comparative analysis,we aimed to validate the effectiveness and precision of the Neuro-11,enhancing its utility in clinical settings.Methods Between November 2020 and December 2021,data were collected from 731 patients receiving outpatient and inpatient care at Shenzhen People’s Hospital in China for various physical discomforts.The patients completed multiple questionnaires,including the Neuro-11,Short Form 36 Health Survey,Patient Health Questionnaire 15 items,Hamilton Anxiety Scale and Hamilton Depression Scale.Psychiatry-trained clinicians conducted structured interviews and clinical examinations to establish a gold standard diagnosis of SSD.Results The Neuro-11 demonstrated strong content reliability and structural consistency,correlating significantly with internationally recognised and widely used questionnaires.Despite its brevity,the Neuro-11 exhibited significant correlations with other questionnaires.A test-retest analysis yielded a correlation coefficient of 1.00,Spearman-Brown coefficient of 0.64 and Cronbach’sαcoefficient of 0.72,indicating robust content reliability and internal consistency.Confirmatory factor analysis confirmed the validity of the three-dimensional structure(p<0.001,comparative fit index=0.94,Tucker-Lewis index=0.92,root mean square error of approximation=0.06,standardised root mean square residual=0.04).The threshold of the Neuro-11 is set at 10 points based on the maximum Youden’s index from the receiver operating characteristic curve analysis.In terms of diagnostic efficacy,the Neuro-11 has an area under the curve of 0.67.

The Co-Evolution between Remittance Business for Overseas Chinese and Institutions： The Case of Chaoshan Region During 1860-1949

This study proposes a framework to analyze the co-evolution between the remittance business for overseas Chinese and their institutions during 1860-1949. In particular, this paper focuses on the co-evolutions between their organizational fields and institutions. It shows that participants communicate, compete and cooperate through their organizational fields, and finally promote the remittance business. Since the three pillars of institutions---regulative, normative, and cultural-cognitive--correlate and interact with each other, it is found that institutions can promote the business of overseas remittance if the combination of these three pillars of institutions works well; otherwise, it ends the business with confusion.

基于FrFT与Kalman滤波的机动目标序列ISAR成像算法

序列逆合成孔径雷达(Inverse Synthetic Aperture Radar,ISAR)指雷达对目标在较长一段时间内连续观测,近实时生成目标的ISAR图像序列。通过这些图像序列能够充分提取目标的散射信息和运动状态变化信息,对目标监测、跟踪和识别有很大帮助。研究充分利用相邻帧间目标运动的连续性,联合分数阶傅里叶变换(Fractional Fourier Transform,FrFT)和卡尔曼(Kalman)滤波技术,提出一种新的机动目标序列ISAR成像算法。该算法对方位向信号进行FrFT,通过搜索FrFT最佳能量聚集对应旋转角实现对方位向信号调频率(Azimuthal Chirp Rate,ACR)的测量,然后使用Kalman滤波技术得到ACR的最优估计值,实现对方位向信号精确的相位补偿。基于当前帧Kalman滤波得到的ACR最优估计值可以减小下一帧FrFT旋转角的搜索范围,提高计算效率。通过实测数据验证了该算法的有效性。相比常用的FrFT参数估计算法和PGA算法,该算法在成像质量和计算效率上均展现了优势。