# Information Fusion for Multi-Source Material Data: Progress and Challenges
## 1 Introduction  
Multi-source material data integration [1] and information fusion is one of the most important techniques that support the Materials Genome Initiative. It is also the foundation for achieving material computing with distinctive features, integration of experiment data and industrial data, and knowledge discovery from material data. The former U.S. president [PERSON] clearly pointed out that the general purpose of the Materials Genome Initiative was to double the rate of discovering, developing, making and using advanced materials. Multi-source data fusion can help us to discover new materials, as well as to advance the development of materials. The multi-source property of material data means that material data usually come from different application domains. Multi-source material data are often heterogeneous, meaning that material data from different sources vary in data format, data description, data quality and data processing methods. Materials are substances that people can use to make useful parts, devices and things. The development of materials plays a key role in the progress of human society. Material science involves the nature and applications of substances in every scientific and engineering field. Material science is an interdisciplinary science that studies the interrelation of material processing technologies, material microstructures and material macro performance. Many kinds of theories are involved in material science, including solid-state physics, material chemistry, mechanical engineering, biology, electrical engineering and computer science. It is a widely accepted idea that integrating material science with other disciplines may lead to new findings or materials. For example, when combined with electronic engineering, material science can derive electronic material; when combined with mechanics, material science can derive structural material. Regarding the integration of material science with computer science, it has been a hot trend to discover new rules from heterogeneous multi-source material data, as multi-source material data contain rich information about material structure, material molecular formula, chemical reactions between materials, etc. However, multi-source material data fusion is not a trial task. First of all, multi-source heterogeneous data usually have different formats, including structural data, semi-structural data and unstructured data. To realize multi-source heterogeneous data integration and fusion, a unified representation framework needs to be designed first [2]. Material data differ from other kinds of data in many aspects. First, material data are messy with complex correlations. Second, the semantic operations among material data sources are complex. Third, material data have high and inconsistent dimensions, meaning that there are not only one-dimension data, but also two-dimension data, three-dimension data, or even high-dimension data. Data fusion first needs to integrate the multi-source heterogeneous data into a unified data processing system. Another challenge lies in the accuracy of material ontology mapping. One kind of material can be represented in different languages, and its scientific name can change with time. In addition, acronyms and synonyms are common in material science. Note that it is already a big challenge in computer science to correctly analyze data with different encodings and formats. Thus, how to resolve conflicts that exist in multi-source material data is a big problem. To the best of our knowledge, the community is currently lacking a comprehensive review of the state-of-the-art techniques on multi-source material data fusion. Although there are a few related reviews in this area, most of them are focused upon specific application domains. Moreover, new information technologies, like artificial intelligence algorithms [3] and big data technologies [4], are not reflected in previous reviews.