In this study, a framework for the multi-objective materials discovery based on Bayesian approaches is developed. The capabilities of the framework are demonstrated on an example case related to the discovery of precipitation strengthened NiTi shape memory alloys with up to three desired properties. In the presented case the framework is used to carry out an efficient search of the shape memory alloys with desired properties while minimizing the required number of computational experiments. The developed scheme features a Bayesian optimal experimental design process that operates in a closed loop. A Gaussian process regression model is utilized in the framework to emulate the response and uncertainty of the physical/computational data while the sequential exploration of the materials design space is carried out by using an optimal policy based on the expected hyper-volume improvement acquisition function. This scalar metric provides a measure of the utility of querying the materials design space at different locations, irrespective of the number of objectives in the performed task. The framework is deployed for the determination of the composition and microstructure of precipitation-strengthened NiTi shape memory alloys with desired properties, while the materials response as a function of microstructure is determined through a thermodynamically-consistent micromechanical model.

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Over the last decade, there has been a paradigm shift away from labor-intensive and time-consuming materials discovery methods, and materials exploration through informatics approaches is gaining traction at present. Current approaches are typically centered around the idea of achieving this exploration through high-throughput (HT) experimentation/computation. Such approaches, however, do not account for the practicalities of resource constraints which eventually result in bottlenecks at various stage of the workflow. Regardless of how many bottlenecks are eliminated, the fact that ultimately a human must make decisions about what to do with the acquired information implies that HT frameworks face hard limits that will be extremely difficult to overcome. Recently, this problem has been addressed by framing the materials discovery process as an optimal experiment design problem. In this article, we discuss the need for optimal experiment design, the challenges in it's implementation and finally discuss some successful examples of materials discovery via experiment design.

Mobile computing adds computing, storage, processing and other functions in wireless network ends to provide customized and differentiated services, so that it can be widely applied in the fields of internet of things, video, medical treatment, retail and so on. Recently, power spatio-temporal big data (PSTBD) technology of smart grid based on mobile computing has experienced explosive growth. This paper emphasizes the specific requirements, technologies, applications, and challenges of the current PSTBD for mobile computing in smart grid. Based on current development status of PSTBD companies in representative countries in the world, we introduce PSTBD technology based on the characteristics of mobile computing based smart grid, and conduct a comprehensive investigation and analysis of relevant articles in this field. After comparing the differences between the traditional and the PSTBD based platform in the aspects of important features, platform goal, and platform architecture, we describe the key technologies and algorithms of the current PSTBD in detail. Then, based on the requirements of each link and field of the power grid, the typical application of PSTBD technology in various aspects of smart grid application based on mobile computing is discussed. Finally, the development direction and challenges of PSTBD are given. Through data analysis and technical discussion, it hopes to provide technical support and decision support for relevant practitioners in the PSTBD field.INDEX TERMS Mobile computing, data processing, smart grids, spatio-temporal big data. I. INTRODUCTIONPower spatio-temporal big data based on mobile computing refers to ''power+mobile equipment+big data'', which collects and processes multi-source, heterogeneous, multi-dimensional and multi-form spatio-temporal big data in various links from generation, transmission, transformation, distribution, power consumption to dispatching power production and power service. The characteristics of Power Spatio-Temporal Big Data (PSTBD) meet the ''5V3E'' characteristics [4]-[6], as shown in Fig. 1. In addition to ''3E''The associate editor coordinating the review of this manuscript and approving it for publication was Xuxun Liu .which is energy, exchange, and empathy, the ''5V'' is as follows.Volume: Conventional power dispatching system includes hundreds of thousands of data collection points; the number of power distribution data centers often reaches tens of millions; data volume is often above TB and PB.Velocity: Decision support requires analysis of large amounts of data in a fraction of a second; real-time processing requires continuous real-time data generation.Variety: Data types are structured, semi-structured, and unstructured data, including real-time data, historical data, text data, multimedia data, time-series data and so on.

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