Price prediction of power transformer materials based on CEEMD and GRU

The rapid growth of the Chinese economy has fueled the expansion of power grids.Power transformers are key equipment in power grid projects,and their price changes have a significant impact on cost control.However,the prices of power transformer materials manifest as nonsmooth and nonlinear sequences.Hence,estimating the acquisition costs of power grid projects is difficult,hindering the normal operation of power engineering construction.To more accurately predict the price of power transformer materials,this study proposes a method based on complementary ensemble empirical mode decomposition(CEEMD)and gated recurrent unit(GRU)network.First,the CEEMD decomposed the price series into multiple intrinsic mode functions(IMFs).Multiple IMFs were clustered to obtain several aggregated sequences based on the sample entropy of each IMF.Then,an empirical wavelet transform(EWT)was applied to the aggregation sequence with a large sample entropy,and the multiple subsequences obtained from the decomposition were predicted by the GRU model.The GRU model was used to directly predict the aggregation sequences with a small sample entropy.In this study,we used authentic historical pricing data for power transformer materials to validate the proposed approach.The empirical findings demonstrated the efficacy of our method across both datasets,with mean absolute percentage errors(MAPEs)of less than 1%and 3%.This approach holds a significant reference value for future research in the field of power transformer material price prediction.

Toward Next-Generation Heterogeneous Catalysts:Empowering Surface Reactivity Prediction with Machine Learning

Heterogeneous catalysis remains at the core of various bulk chemical manufacturing and energy conversion processes,and its revolution necessitates the hunt for new materials with ideal catalytic activities and economic feasibility.Computational high-throughput screening presents a viable solution to this challenge,as machine learning(ML)has demonstrated its great potential in accelerating such processes by providing satisfactory estimations of surface reactivity with relatively low-cost information.This review focuses on recent progress in applying ML in adsorption energy prediction,which predominantly quantifies the catalytic potential of a solid catalyst.ML models that leverage inputs from different categories and exhibit various levels of complexity are classified and discussed.At the end of the review,an outlook on the current challenges and future opportunities of ML-assisted catalyst screening is supplied.We believe that this review summarizes major achievements in accelerating catalyst discovery through ML and can inspire researchers to further devise novel strategies to accelerate materials design and,ultimately,reshape the chemical industry and energy landscape.

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.

Failure evolution and disaster prediction of rock under uniaxial compression based on non-extensive statistical analysis of electric potential

Rock failure can cause serious geological disasters,and the non-extensive statistical features of electric potential(EP)are expected to provide valuable information for disaster prediction.In this paper,the uniaxial compression experiments with EP monitoring were carried out on fine sandstone,marble and granite samples under four displacement rates.The Tsallis entropy q value of EPs is used to analyze the selforganization evolution of rock failure.Then the influence of displacement rate and rock type on q value are explored by mineral structure and fracture modes.A self-organized critical prediction method with q value is proposed.The results show that the probability density function(PDF)of EPs follows the q-Gaussian distribution.The displacement rate is positively correlated with q value.With the displacement rate increasing,the fracture mode changes,the damage degree intensifies,and the microcrack network becomes denser.The influence of rock type on q value is related to the burst intensity of energy release and the crack fracture mode.The q value of EPs can be used as an effective prediction index for rock failure like b value of acoustic emission(AE).The results provide useful reference and method for the monitoring and early warning of geological disasters.

A new analytical model for thermal stresses in multi-phase materials and lifetime prediction methods

Based on the fundamental equations of the mechanics of solid continuum, the paper employs an analytical model for determination of elastic thermal stresses in isotropic continuum represented by periodically distributed spherical particles with different distributions in an infinite matrix, imaginarily divided into identical cells with dimensions equal to inter-particle distances, containing a central spherical particle with or without a spherical envelope on the particle surface. Consequently, the multi-particle-（envelope）- matrix system, as a model system regarding the analytical modelling, is applicable to four types of multi-phase materials. As functions of the particle volume fraction v, the inter-particle distances dl, d2, d3 along three mutually per- pendicular axes, and the particle and envelope radii, R1 and R2, respectively, the thermal stresses within the cell, are originated during a cooling process as a consequence of the difference in thermal expansion coefficients of phases rep- resented by the matrix, envelope and particle. Analytical-（experimental）-computational lifetime prediction methods for multi-phase materials are proposed, which can be used in engineering with appropriate values of parameters of real multi-phase materials.

Recent progress on the prediction of two-dimensional materials using CALYPSO

In recent years, structure design and predictions based on global optimization approach as implemented in CALYPSO software have gained great success in accelerating the discovery of novel two-dimensional(2D) materials. Here we highlight some most recent research progress on the prediction of novel 2D structures, involving elements, metal-free and metal-containing compounds using CALYPSO package. Particular emphasis will be given to those 2D materials that exhibit unique electronic and magnetic properties with great potentials for applications in novel electronics, optoelectronics,magnetronics, spintronics, and photovoltaics. Finally, we also comment on the challenges and perspectives for future discovery of multi-functional 2D materials.

Machine learning of materials design and state prediction for lithium ion batteries

With the widespread use of lithium ion batteries in portable electronics and electric vehicles,further improvements in the performance of lithium ion battery materials and accurate prediction of battery state are of increasing interest to battery researchers.Machine learning,one of the core technologies of artificial intelligence,is rapidly changing many fields with its ability to learn from historical data and solve complex tasks,and it has emerged as a new technique for solving current research problems in the field of lithium ion batteries.This review begins with the introduction of the conceptual framework of machine learning and the general process of its application,then reviews some of the progress made by machine learning in both improving battery materials design and accurate prediction of battery state,and finally points out the current application problems of machine learning and future research directions.It is believed that the use of machine learning will further promote the large-scale application and improvement of lithium-ion batteries.

Neural network-based model for prediction of permanent deformation of unbound granular materials

Several available mechanistic-empirical pavement design methods fail to include predictive model for permanent deformation(PD)of unbound granular materials(UGMs),which make these methods more conservative.In addition,there are limited regression models capable of predicting the PD under multistress levels,and these models have regression limitations and generally fail to cover the complexity of UGM behaviour.Recent researches are focused on using new methods of computational intelligence systems to address the problems,such as artificial neural network(ANN).In this context,we aim to develop an artificial neural model to predict the PD of UGMs exposed to repeated loads.Extensive repeated load triaxial tests(RLTTs)were conducted on base and subbase materials locally available in Victoria,Australia to investigate the PD properties of the tested materials and to prepare the database of the neural networks.Specimens were prepared over different moisture contents and gradations to cover a wide testing matrix.The ANN model consists of one input layer with five neurons,one hidden layer with twelve neurons,and one output layer with one neuron.The five inputs were the number of load cycles,deviatoric stress,moisture content,coefficient of uniformity,and coefficient of curvature.The sensitivity analysis showed that the most important indicator that impacts PD is the number of load cycles with influence factor of 41%.It shows that the ANN method is rapid and efficient to predict the PD,which could be implemented in the Austroads pavement design method.

Machine learning techniques for prediction of capacitance and remaining useful life of supercapacitors: A comprehensive review

Supercapacitors are appealing energy storage devices for their promising features like high power density,outstanding cycling stability,and a quick charge–discharge cycle.The exceptional life cycle and ultimate power capability of supercapacitors are needed in the transportation and renewable energy generation sectors.Hence,predicting the capacitance and lifecycle of supercapacitors is significant for selecting the suitable material and planning replacement intervals for supercapacitors.In addition,system failures can be better addressed by accurately forecasting the lifecycle of SCs.Recently,the use of machine learning for performance prediction of energy storage materials has drawn increasing attention from researchers globally because of its superiority in prediction accuracy,time efficiency,and costeffectiveness.This article presents a detailed review of the progress and advancement of ML techniques for the prediction of capacitance and remaining useful life(RUL)of supercapacitors.The review starts with an introduction to supercapacitor materials and ML applications in energy storage devices,followed by workflow for ML model building for supercapacitor materials.Then,the summary of machine learning applications for the prediction of capacitance and RUL of different supercapacitor materials including EDLCs(carbon based materials),pesudocapacitive(oxides and composites)and hybrid materials is presented.Finally,the general perspective for future directions is also presented.

Machine Learning-Assisted High-Throughput Virtual Screening for On-Demand Customization of Advanced Energetic Materials

Finding energetic materials with tailored properties is always a significant challenge due to low research efficiency in trial and error.Herein,a methodology combining domain knowledge,a machine learning algorithm,and experiments is presented for accelerating the discovery of novel energetic materials.A high-throughput virtual screening(HTVS)system integrating on-demand molecular generation and machine learning models covering the prediction of molecular properties and crystal packing mode scoring is established.With the proposed HTVS system,candidate molecules with promising properties and a desirable crystal packing mode are rapidly targeted from the generated molecular space containing 25112 molecules.Furthermore,a study of the crystal structure and properties shows that the good comprehensive performances of the target molecule are in agreement with the predicted results,thus verifying the effectiveness of the proposed methodology.This work demonstrates a new research paradigm for discovering novel energetic materials and can be extended to other organic materials without manifest obstacles.

MatCloud, a high-throughput computational materials infrastructure： Present, future visions, and challenges

MatCloud provides a high-throughput computational materials infrastructure for the integrated management of materials simulation, data, and computing resources. In comparison to AFLOW, Material Project, and NoMad, MatCloud delivers two-fold functionalities： a computational materials platform where users can do on-line job setup, job submission and monitoring only via Web browser, and a materials properties simulation database. It is developed under Chinese Materials Genome Initiative and is a China own proprietary high-throughput computational materials infrastructure. MatCloud has been on line for about one year, receiving considerable registered users, feedbacks, and encouragements. Many users provided valuable input and requirements to MatCloud. In this paper, we describe the present MatCloud, future visions, and major challenges. Based on what we have achieved, we will endeavour to further develop MatCloud in an open and collaborative manner and make MatCloud a world known China-developed novel software in the pressing area of high-throughput materials calculations and materials properties simulation database within Material Genome Initiative.

Discovery and design of lithium battery materials via high-throughput modeling

This paper reviews the rapid progress in the field of high-throughput modeling based on the Materials Genome Initiative, and its application in the discovery and design of lithium battery materials. It offers examples of screening, optimization and design of electrodes, electrolytes, coatings, additives, etc. and the possibility of introducing the machine learning method into material design. The application of the material genome method in the development of lithium battery materials provides the possibility to speed up the upgrading of new candidates in the discovery of lots of functional materials.

High-throughput computational screening and design of nanoporous materials for methane storage and carbon dioxide capture

The globally increasing concentrations of greenhouse gases in atmosphere after combustion of coal-or petroleum-based fuels give rise to tremendous interest in searching for porous materials to efficiently capture carbon dioxide(CO_2) and store methane(CH4), where the latter is a kind of clean energy source with abundant reserves and lower CO_2 emission. Hundreds of thousands of porous materials can be enrolled on the candidate list, but how to quickly identify the really promising ones, or even evolve materials(namely, rational design high-performing candidates) based on the large database of present porous materials? In this context, high-throughput computational techniques, which have emerged in the past few years as powerful tools, make the targets of fast evaluation of adsorbents and evolving materials for CO_2 capture and CH_4 storage feasible. This review provides an overview of the recent computational efforts on such related topics and discusses the further development in this field.

High-throughput theoretical design of lithium battery materials

The rapid evolution of high-throughput theoretical design schemes to discover new lithium battery materials is re- viewed, including fiigh-capacity cathodes, low-strain cathodes, anodes, solid state eleclrolytes, and electrolyte additives. With tfie development of efficient theoretical methods and inexpensive computers, high-throughput theoretical calculations have played an increasingly important role in the discovery of new malerials. With the help of automatic simnlation flow, many types of materials can be screened, optimized and designed from a structural database according to specific search criteria. In advanced cell technology, new materials for next generation lithium batteries are of great significance to achieve perlbmmnce, and some representative criteria are： higher energy density, better safety, and faster charge/discharge speed.

Deep learning in two-dimensional materials:Characterization,prediction,and design

Since the isolation of graphene,two-dimensional(2D)materials have attracted increasing interest because of their excellent chemical and physical properties,as well as promising applications.Nonetheless,particular challenges persist in their further development,particularly in the effective identification of diverse 2D materials,the domains of large-scale and highprecision characterization,also intelligent function prediction and design.These issues are mainly solved by computational techniques,such as density function theory and molecular dynamic simulation,which require powerful computational resources and high time consumption.The booming deep learning methods in recent years offer innovative insights and tools to address these challenges.This review comprehensively outlines the current progress of deep learning within the realm of 2D materials.Firstly,we will briefly introduce the basic concepts of deep learning and commonly used architectures,including convolutional neural and generative adversarial networks,as well as U-net models.Then,the characterization of 2D materials by deep learning methods will be discussed,including defects and materials identification,as well as automatic thickness characterization.Thirdly,the research progress for predicting the unique properties of 2D materials,involving electronic,mechanical,and thermodynamic features,will be evaluated succinctly.Lately,the current works on the inverse design of functional 2D materials will be presented.At last,we will look forward to the application prospects and opportunities of deep learning in other aspects of 2D materials.This review may offer some guidance to boost the understanding and employing novel 2D materials.

Geoscience material structures prediction via CALYPSO methodology

Many properties of planets such as their interior structure and thermal evolution depend on the high-pressure properties of their constituent materials. This paper reviews how crystal structure prediction methodology can help shed light on the transformations materials undergo at the extreme conditions inside planets. The discussion focuses on three areas:(i) the propensity of iron to form compounds with volatile elements at planetary core conditions(important to understand the chemical makeup of Earth's inner core),(ii) the chemistry of mixtures of planetary ices(relevant for the mantle regions of giant icy planets), and(iii) examples of mantle minerals. In all cases the abilities and current limitations of crystal structure prediction are discussed across a range of example studies.

High-throughput design of functional materials using materials genome approach

High-throughput computational materials design provides one efficient solution to accelerate the discovery and development of functional materials. Its core concept is to build a large quantum materials repository and to search for target materials with desired properties via appropriate materials descriptors in a high-throughput fashion, which shares the same idea with the materials genome approach. This article reviews recent progress of discovering and developing new functional materials using high-throughput computational materials design approach. Emphasis is placed on the rational design of high-throughput screening procedure and the development of appropriate materials descriptors, concentrating on the electronic and magnetic properties of functional materials for various types of industrial applications in nanoelectronics.

Designing solar-cell absorber materials through computational high-throughput screening

Although the efficiency of CH3 NH3 PI3 has been refreshed to 25.2%,stability and toxicity remain the main challenges for its applications.The search for novel solar-cell absorbers that are highly stable,non-toxic,inexpensive,and highly efficient is now a viable research focus.In this review,we summarize our recent research into the high-throughput screening and materials design of solar-cell absorbers,including single perovskites,double perovskites,and materials beyond Perovskites.BazrS3(single perovskite),Ba2 BiNbS6(double perovskite),HgAl2 Se4(spinel),and IrSb3(skutterudite)were discovered to be potential candidates in terms of their high stabilities,appropriate bandgaps,small carrier effective masses,and strong optical absorption.

Theoretical design of multifunctional half-Heusler materials based on first-principles calculations

The family of ABX half-Heusler materials, also called filled-tetrahedral structures, is a special class of ternary compounds hosting a variety of material functionalities including thermoelectricity, topological insulation, magnetism, transparent conductivity and superconductivity. This class of compounds can be derived from two substitution approaches, i.e.,from Heusler materials by removing a portion of atoms forming ordered vacancies thus becoming half-Heusler, or from tetrahedral zinc blende compounds by adding atoms on the interstitial sites thus become filled-tetrahedral structures. In this paper, we briefly review the substitution approaches for material design along with their application in the design of half-Heusler compounds; then we will review the high-throughput search of new half-Heusler filled-tetrahedral structures and the study of their physical properties and functionalities.

Estimation and prediction of plastic waste annual input into the sea from China

Marine plastic debris has been a pervasive issue since the last century, and research on its sources and fates plays a vital role in the establishment of mitigation measures. However, data on the quantity of plastic waste that enters the sea on a certain timescale remain largely unavailable in China. Here, we established a model using material flow analysis method based on life cycle assessment to follow plastic product from primary plastic to plastic waste with statistical data and monitoring data from accurate sources. This model can be used to estimate and forecast the annual input of plastic waste into the sea from China until 2020. In 2011, 0.547 3-0.751 5 million tons of plastic waste entered the seas in China, with a growth rate of 4.55% per year until 2017. And the amount will decrease to 0.257 1 to 0.353 1 million tons in 2020 under the influence of governmental management. The amount of plastic waste discharged from coastal areas calculated in this study was much larger than that from river, thus it is suggested to strengthen the governance and control of plastic waste in coastal fishery activities in China in order to reduce the amount of marine plastic waste input.