InterMat: A Blockchain-Based Materials Data Discovery and Sharing Infrastructure

Abstract

Material research and development driven by data analysis necessitates a substantial volume of data. However, conventional material data sharing platforms encounter challenges in sharing and integrating data across multiple platforms. This article proposes a blockchain-based materials data discovery and sharing infrastructure—InterMat, which is a material big data management and sharing framework model integrating cloud platforms and blockchain. It could support the full lifecycle of materials data sharing, including data generation, management, discovery, sharing, traceability, and valuation. The architecture of the InterMat, its unique method of constructing a consortium chain, and the protocol for data discovery are presented in this paper. Additionally, the method for materials data identifier and blockchain certification is established, which allows for a unified identifier on the blockchain and cloud-based data addressing from various organizations. InterMat has data discovery algorithms for various materials to achieve the discovery of similar materials data from different nodes. Furthermore, we have designed some blockchain smart contracts for InterMat to encourage data sharing across nodes. These contracts include a proof smart contract that records data sharing activities, ensuring transparency and traceability in the materials data flow. The other contract is a value-estimating contract to encourage high-quality data sharing. Finally, this article introduces the application case of InterMat, using steel materials as an example to demonstrate its applications in data management, data discovery, data valuation, etc. This study successfully addresses various challenges associated with the cross-platform sharing of materials data, such as issues related to data discovery, data rights and control, and willingness to share. InterMat can assist material researchers in discovering and accessing more data, which would create a new ecology for sharing data.

1. Introduction

The advancement of the Internet and computer technology has significantly enhanced the data collection and management capabilities of the materials industry [1]. Big data technology allows for the analysis, mining, and application of massive materials data, thereby maximizing their value [2]. Currently, the methodology of materials research is entering the fourth paradigm through empirical science, theoretical science, and computational simulation, which is big-data-driven material research and development [3]. At present, there are many materials data platforms around the world, such as AFLOW [4], NOMAD CoE [5,6], and OQMD [7]. These platforms work variably based on their positions, some of which focus on providing data services, while others provide data analysis and algorithm workflows.

The materials industry is a typical process industry with multiple key stages [8]. The full life cycle of materials involves research and development, production, equipment manufacturing, and service. Various types of data are generated at each stage, and these data are interconnected and complementary. By integrating and conducting multi-dimensional analysis, the full potential of the data can be realized. However, these data are spread across various platforms, and they are operated and owned by different authorities. Therefore, finding a solution to enable data sharing across platforms is the priority issue in materials data infrastructure.

As for material data, sharing across platforms will face several challenges. Firstly, the materials data discovery, which refers to the process of finding similar materials data in different heterogeneous databases, is difficult to deal with due to various data heterogeneity and data standards in different platforms [9]. Thus, to enable materials data sharing, it is fundamental to establish appropriate metadata standards and efficient data discovery algorithms. Secondly, confirming data rights and ensuring controllable sharing is crucial, as materials data are one kind of firsthand scientific data that are collected during research and production. These data are considered as valuable assets [10]. Therefore, it is crucial to ensure the protection of intellectual property rights and allow users to have independent control over the data during its flow. Lastly, the willingness to share, which includes data quality issues and driving forces for sharing, should be well treated. The accuracy of material analysis and evaluation depends on data quality, so a mechanism to assess data reliability should be designed. Additionally, incentive mechanisms should be implemented to encourage sharing behavior.

The sharing and utilization of large amounts of materials data are hindered by the aforementioned issues. However, the emergence of blockchain technology offers new solutions. Blockchain technology is a decentralized database technology that combines various computer technologies, including distributed data storage, peer-to-peer transmission, and encryption algorithms. It uses distributed consensus algorithms to generate and update data and utilizes peer-to-peer networks for data transmission between nodes. The distributed ledger, which combines cryptography theories and timestamps, ensures the tamper-proof and traceability of stored data and utilizes automated script code or smart contracts to implement upper-layer application logic. If traditional databases implement unilateral maintenance of data, blockchain implements multi-party maintenance of the same data to ensure data security and business fairness. For this reason, although blockchain was initially used in digital currencies [11,12,13], its decentralization and traceability make it an effective solution for sharing data across different organizations [14,15,16,17,18,19].

In the industrial field, many researchers are exploring the application of blockchain. Liu et al. [20] proposed a framework for product lifecycle management (PLM) based on industrial blockchain to facilitate data exchange and shared services throughout the product lifecycle. The study introduced the concept of industrial blockchain and provided an information storage and exchange platform that enables cross-enterprise data sharing services. Mandolla et al. [21] proposed a blockchain system for creating digital twins for additive manufacturing and used aviation materials as an example to illustrate the benefits of combining blockchain and 3D printing in coordinating material usage time and supply time. Blockchain can securely transmit data to verified 3D printers, thereby saving costs associated with inventory, procurement, and logistics. Joshi et al. [22] researched the adoption of blockchain technology for privacy and security in the context of Industry 4.0. They have explored the fundamentals of blockchain, blockchain applications, the key costs impacted by blockchain, and privacy-preserving approaches. Furthermore, several possibilities for IoT and Industry 4.0 applications that leverage blockchain are being investigated. Ding et al. [23] proposed a manufacturing system based on blockchain for an I4.0 workshop by studying the autonomous trusted trigger mechanism of smart contracts and the distributed storage method of manufacturing data in a workshop manufacturing system and integrating the workshop service system, a system operation mechanism for manufacturing data security and trusted triggers in the manufacturing process. In general, the current research on blockchain applications in the field of industry is very limited, especially in the field of materials. It is necessary to further explore the characteristics of materials data, data discovery and sharing models, and privacy protection methods in order to promote the sharing and application of materials big data.

This paper introduces a blockchain-based infrastructure called InterMat (as shown in Figure 1). It is built on a blockchain foundation and supports the entire lifecycle of materials data, including generation, management, certification, discovery, sharing, traceability, application, and valuation. InterMat is designed to facilitate data sharing and circulation throughout the entire lifecycle of the materials industry, from research and development to production and service, enabling data-driven material research and development.

2. Construction of InterMat Model

2.1. System Architecture

The diagram in Figure 2 illustrates the structure of the InterMat architecture. It consists of three layers: the client, the cloud platform, and the blockchain. These layers work together and communicate through multiple interfaces.

Client: including data providers and data users. The data provider uploads the data through the client, and the complete data are stored in the cloud platform, while at the same time the metadata are stored in the blockchain. The data user also utilizes the client to request data, which are then sent to the blockchain. The blockchain sends the storage location information to the data requester, and then the data requester obtains the materials data.

The cloud platform: cloud computing provides on-demand network access of configurable computing resources, enabling users to pay for the resources or services they use [24]. The cloud platform offers wide accessibility, abundant resources, flexible distribution, and strong reliability. As a result, many materials data platforms utilize the cloud platform as their infrastructure for storing data. Various organizations store the data from each stage of the material industry chain in different cloud computing platforms. In the cloud platform of InterMat, complete materials data are stored, including metadata and master data. Furthermore, the data release and data flow events can be stored through the certificate storage smart contract to achieve the purpose of data confirmation.

Blockchain: blockchain technology is utilized to create a system of trust between various platforms. A smart contract is employed to extract and encode metadata based on a predefined strategy, which are then stored in the blockchain state database. When a data requester initiates a data request, the blockchain is used for data discovery and addressing. Furthermore, the data proof smart contract can perform blockchain certification on data release and data sharing events to confirm data ownership rights.

2.2. The Construction of Consortium Blockchain

InterMat employs consortium blockchain technology [25] to create a network with three layers: ‘node-industry chain-InterMat’. This network enables the sharing of materials data across different organizations and industries, creating a collaborative ecosystem. The architecture of its application network model is depicted in Figure 3.

• Nodes

Nodes are the most basic units of the InterMat network and can be divided into full nodes and light nodes. A full node is a node that has the function of storing materials data and participates in the construction of a new block. The ledger includes the block header and the block body that stores the list of transactions. Full nodes are suitable for large enterprises, organizations, and institutions with many people. A light node is a node that only stores materials data. The storage method includes independent node servers or third-party public cloud accounts. It does not participate in consensus but receives block header information sent by full-node users. It is suitable for small teams or individuals in scientific research institutions.

• Material industry chain

The material industry chain is composed of multiple nodes. It is a collaborative blockchain network of organizations within a specific industry that work together to share materials data. This industry chain utilizes materials data to connect various enterprise nodes, such as research and development, production, procurement, equipment construction, and application. Taking the petrochemical industry chain as an example, it connects petrochemical material R&D institutions, petrochemical material production plants, petrochemical equipment manufacturing enterprises, and petrochemical terminal enterprises. These organizations can share petrochemical materials data through blockchains and then use data-driven material R&D methods to optimize materials.

• InterMat

InterMat is a blockchain-based network that enables the decentralized or multi-centralized sharing of materials data. It connects nodes and different material industry chains to create a network. This allows for the efficient sharing of materials data between different industries, forming a network ecology for interconnecting multiple industries. InterMat utilizes smart contracts to ensure secure and reliable data management and sharing services. These services include materials data identification and proof, data discovery, shared data traceability, data valuation, and so on.

2.3. Materials Data Discovery Protocol

The data sources and types vary across different stages of the materials industry chain. In the research and development stage, data are primarily sourced from scientific research institutes and universities, encompassing composition, performance, structure, and process-related research data. Material production plants are the main source of data in the production stage, providing data on material production processes, equipment parameters, and factory inspection data. Equipment manufacturing data are mainly obtained from equipment enterprises, including data on material processing, welding, heat treatment, and so on. Material service data primarily originate from downstream application enterprises, consisting of data generated by equipment and materials in service, such as material corrosion and failure data. The combination of heterogeneous data sets from different stages and organizations in the industrial chain can complement each other for a specific material. Establishing a secure sharing mode can significantly enhance research and development efficiency. However, the challenge lies in the fact that data are scattered across different database systems, each using different data standards [26,27]. Therefore, finding and accessing data between different systems is a problem that needs to be addressed.

Metadata refer to information that describes other data [28,29] and are essential for finding and understanding data [30,31]. Traditional metadata typically consist of keywords, but different organizations have different standards for assigning keywords. For instance, a specific type of low-temperature steel with nine percent nickel content may be labeled as 9Ni by institution A, 06Ni9DR by institution B, and X8Ni9 by institution C. This demonstrates that keyword-based matching is not appropriate for sharing materials data across different organizations.

In this paper, we propose a method for discovering and sharing materials data across different nodes using pattern recognition. The method is illustrated in Figure 4. Despite the complexity and various forms of materials data, we categorize them into tetrahedral elements known as material feature data, which include composition, performance, structure, and process [32]. We use multidimensional perception to analyze the materials data, extract the relevant material features, and represent them as a set of mathematical features. By calculating the similarity of material feature data between different materials, we can identify data with high similarity and provide them to the data requester, enabling the retrieval of similar materials. This approach allows for the effective correlation of materials data across different stages of research, development, production, manufacturing, and service. Authorized users can then share and collaborate on the data.

It is worth mentioning that the traditional implementation mode of this algorithm is the index data outsourcing calculation mode, which requires all parties to send material feature data to a third-party server for similarity calculation. However, this centralized model is susceptible to single point failures and lacks the ability to verify the credibility of third-party servers. This article uses distributed ledgers of blockchain for data discovery, which can effectively address the various shortcomings of centralized servers.

3. System Implementation

3.1. Materials Data Identification and Blockchain Certification

Materials data are typically composed of two parts: the master data and the metadata. The master data refer to the main content of the materials data, such as detection reports, calculation results, and production data. They can take various formats, including documents, images, tables, videos, audio files, and so on. Metadata are additional information that describes the material master data. They include materials data attributes, owners, characteristics, and other index information. Metadata serve two purposes in the sharing of data. Firstly, they serve as a means to identify the origin of the data, thereby enhancing the reliability and trustworthiness of the shared information. Secondly, metadata provide a description of the characteristics and attributes of the data, enabling data users to effectively locate and access the desired information.

Since the data come from different data sources, we first need to develop a set of identifier coding rules to uniquely mark the shared data and maintain the information. Then, in order to achieve the addressing of shared data, it is necessary to develop a set of identifier resolution methods, resolve the identifier according to a standardized process, and locate the location of shared data. Metadata are the main component of the identifier. In InterMat, we divide the metadata of materials into metadata of data source and metadata of master data.

Each materials data platform can include multiple data sources, so it is necessary to describe the data sources through metadata, as shown in

Table 1. The metadata table of the data source.

No.	Components	Explanation
1	SourceID	ID of data source, unique identifier of materials data source
2	SourceType	Data source types, including calculation software, laboratory, IoT devices, public data, and so on
3	SourceName	The specific name of the data source
4	Provider	The provider of the data source
5	CollectTime	Start time of data collection
6	CollectURL	Unified Resource Identification for data collection

. The example is shown in Box 1.

Box 1. Example: Experimental Data of R&D Data Platform from X company.

{

 “MaterialDataCollect”:

 {

 “SourceID”: “Steel_SEM photo of_R&D Data Platform of X company_definite interval”,

 “SourceType”: “Lab”

 “SourceName”: “S-4300 SEM”

 “Provider”: “Testing Technology Center of X company”

 “CollectionTime”: “YYYYMMDDThhmmss”

 “CollectURL”: “https://www.xxxxx.com/xxxxx”

 }

}

The metadata of the master data are used to describe the information of the master data, including the ownership information of the data, the index information used for data discovery, the location of the data, etc., as shown in

Table 2. The metadata table of the master data.

No.	Components	Explanation
1	DataID	ID of data, unique identifier of materials data
2	DataName	Name of materials data
3	DataType	The format of the master data file, such as text, tables, images, etc.
4	Unit	Unit of measurement for materials data, such as KB, MB, GB, etc.
5	Size	Materials data size
6	Owner	Owner of materials data
7	CreatTime	Data creation time
8	MaterialName	Name of the material
9	MaterialType	Type of the material
10	MaterialFeature	Material feature data, as shown in Table 3
11	Description	Remarks on materials data

.

In the metadata table of the master data, material feature data are essential. They are also the core of the materials data discovery protocol, serving as an index for material discovery calculations. Material feature data are the basic features of materials, which can be summarized as tetrahedral elements of materials, including composition, property, structure, and process, as shown in

Table 3. Material feature data table.

No.	Components	Explanation
1	DataID	ID of data, unique identifier of materials data
2	Compositions	Chemical compositions data
3	Properties	Mechanical properties, physical properties, application performance, etc.
4	Processes	Molding process, heat treatment process, etc.
5	Structures	Crystal structure, phase structure, organization, etc.
6	DataID	ID of data, unique identifier of materials data

. The example is shown in Box 2.

Box 2. Example: SEM photo of pipeline steel.

{

 “MaterialData”:

 {

  “DataID”: “DB74989E-C6AA-4E3B-B209-04550154AC46”,

  “DataName”: “SEM photo of pipeline steel”

  “DataType”: “.jpg”

  “Unit”: “MB”

  “Size”: “25.2”

  “Owner”: “Research Institute of X company”

  “CreateTime”: “YYYYMMDDThhmmss”

  “MaterialName”: “06Ni9DR”

  “MaterialType”: “Low temperature steel”

  “MaterialFeature”: “C0.06Si0.3Mn0.5Ni9Cr0.2Mo0.1Cu0.2V0.01”

  “Description”: “This data is a SEM photo of X80 thermal simulation sample”

 }

}

Material metadata should be stored on the blockchain and stored in the blockchain status database. On the one hand, the data release time and content should be recorded and the data rights verified; on the other hand, metadata are also used for data discovery calculations to discover materials data with similar properties on the chain.

The process of materials data identification, certification, and resolution is shown in the Figure 5.

• Data storage: The materials data are uploaded by the data provider to the cloud, where they are stored and a URL is provided. Afterwards, the data provider sends a request to the blockchain, including metadata from Table 1 and Table 2, material feature data from Table 3, master data hash, and data signature. According to the identification rules, the blockchain generates the TxID, user identity certificate hash (CaHash), and transaction hash of the transaction (TxHash). Then the TxID and the TxHash are returned to the data provider as the result of the blockchain certification.
• Data request: The data user makes a request to the blockchain for the data. The blockchain then finds the necessary data based on the data discovery protocol, resolves the URL using the identifier resolution rules, and sends the data back to the user. Data users use this URL request to access materials data in the cloud platform.

3.2. Materials Data Discovery Algorithm

Data users can initiate data discovery requests based on their own materials data and discover similar materials data in InterMat. The discovery of materials data can be realized by algorithms, such as material pattern recognition based on the data schema of material metadata and feature data. The algorithm mainly includes two parts: material feature data extraction and data similarity calculation.

• Material feature data extraction

Materials data are abstracted as a set of feature vectors, including composition, properties, and keywords (process, structure, shape), in the following form:

$$X_i=\left[x_{i1},x_{i2},\dots ,x_{iN}\right]$$

(1)

$$K_i=\left[k_{i1},k_{i2},\dots ,k_{in}\right]$$

(2)

where:

N—the number of components (or the number of performance indicators);

x_ij—the value of the component (or property) j in the material i;

n—the total number of keywords.

• Data similarity calculation

First, we need to normalize the data.

$$Y_{ij}=\frac{X_{ij}-X_j^{\min }}{X_j^{\max }-X_j^{\min }}\hspace{1em}0\le Y_{ij}\le 1$$

(3)

where:

X_ij—the jth component (or property) data of material i;

$X_j^{\max }$—the maximum value of the component (or performance) j in the data set;

$X_j^{\min }$—the minimum value of the component (or performance) j in the dataset.

Second, we need to calculate the similarity of the data. For the two sets of materials data A and B from different nodes, we recommend the distance function method to calculate the similarity:

$$DS{C}_{AB}=1-\sqrt{\frac{{\displaystyle \sum _{j=1}^N{(Y_{Bj}-Y_{Aj})}^2}}{N}}$$

(4)

where:

Y_j—the data after the jth component standardization;

N—the total number of composition.

The calculation of mechanical properties similarity DSP is the same as that of DSC. The keyword similarity DSK_AB is calculated as follows:

$$DS{K}_{AB}=\frac{N_{matched}}{N_{total}}$$

(5)

where:

N_matched—the number of matched keywords in materials A and B;

N_total—the total number of keywords that participated in the match.

Then the total similarity:

$${DS}_{AB}={DSC}_{AB}\times {DSP}_{AB}\times {DSK}_{AB}$$

(6)

By calculating the similarity of the materials that can be found in each node one by one, sorting the data according to the similarity from high to low, and selecting the data with the highest similarity for display, the function of data association of similar materials in each node can be realized.

If there is a need for higher data privacy protection and the material feature data and other metadata used as an index also need to be kept confidential, we can use privacy calculations or searchable encryption methods to perform confidential calculations on the data. There are various encryption methods available, and we take the inner product encryption approach as an example to explain the similarity calculation method of material feature data in the encryption state in this paper. Inner product encryption is a type of function encryption that supports nearest neighbor search of data in an encrypted state [33,34], and it is suitable for scenarios where a similarity calculation of material feature data is performed. The process is illustrated in Figure 6.

When encryption calculation is required for material feature data, a trusted organization needs to be used as the key distribution node in the system. The key distribution node is responsible for initializing the system and generating the public key (pk) and master private key (msk). Then, the data provider encrypts the material feature data y through msk to generate ciphertext ct_y. Then the data provider sends the ct_y to the blockchain, which stores the ct_y and pk. When the data user initiates a data discovery request based on the material feature data x, the request is sent to the key distribution node, which sends the msk to the data user. The data user encrypts the feature data x to obtain the ciphertext sk_x, which is then sent to the blockchain. Blockchain uses pk, sk_x, and ct_y for decryption calculations to obtain the inner product values of x and y. Then, through calculation, the similarity between the two material feature data is obtained in plaintext.

3.3. Materials Data Traceability

Ensuring the traceability of materials data is crucial for facilitating data sharing among different organizations. To enhance the credibility of the data, we utilize blockchain technology to document the data flow process. In line with the characteristics of the materials data sharing system, this article proposes the creation of a data chain and a behavior chain, as depicted in Figure 7. The data chain primarily captures version updates of the data, while the behavior chain focuses on recording data flow events. The events recorded in the data chain and behavior chain are stored in the blockchain, making it difficult to tamper with and enabling reliable traceability of data during sharing.

The data chain records the modifications made by each user to the data. The data chain mainly includes:

• Data identifier;
• The version number of the current version;
• Data hash;
• Timestamp: the time of initial upload and the time of each modification.

The behavior chain records the sharing events of data, such as exchanges, references, etc. After discovering the data, the data user can request permission from the data owner. After being authorized by the data owner, data can be shared with the data user. Sharing, browsing, and other behaviors are recorded through transaction logs and stored in the blockchain, which is difficult to tamper with. The behavior chain mainly includes:

• Data identifier;
• Owner of data;
• User: the user who requests data access;
• Event types: including browsing data, providing feedback on data quality (positive feedback, negative feedback), and citing data;
• Timestamp: the time when the behavior occurred.

The blockchain network regularly packages the data chain and behavior chain data and forms a chain structure. A block consists of a block header and a block body. The block header records the block height, the hash of the previous block, the Merkel root, and the timestamp. In this way, the blockchain certification of materials data in the process of sharing and transmission is realized, and the right to use and ownership of data are clarified, making the data sharing more secure and credible.

3.4. Data Valuation

The value of materials data needs to be evaluated in terms of data quality, data influence, and other aspects. Traditional data evaluation relies on expert judgment, which is inefficient and has large subjective factors. In this paper, we model the value of data through interactive data (as shown in

Table 4. The structure of the data value table.

No.	Project	Symbol
1	The number of exchanged	E
2	Number of referenced	R
3	Positive feedback	Fp
4	Negative feedback	Fn
5	Page view	P
6	Data value	V

), such as browsing, referencing, feedback, and so on, and objectively calculate the value of the data. Interactive data can indirectly reflect the importance and quality of data. Valuing materials data through interactive data offers two benefits. Firstly, it fosters a community atmosphere that boosts user engagement and encourages data sharing. Secondly, by using blockchain smart contracts to label low-value data, it incentivizes the sharing of high-value data, leading to improved data quality and the establishment of a high-quality data sharing ecosystem. It is worth mentioning that since the interactive data have already been verified in the blockchain, the data are reliable, so the calculated materials data value is also reliable.

The data value V can be calculated through the items in the above table:

$$\mathrm{V}=\mathrm{E}+\mathrm{R}+{\mathrm{F}}_{\mathrm{P}}-{\mathrm{F}}_{\mathrm{n}}+{\log }_2\left(\mathrm{P}+1\right)$$

(7)

This model is an initial model, which can be adjusted according to the actual situation in combination with the machine learning algorithm after the data are online.

This model is the initial model and provides a feasible model for calculating the value of data. In the future, we can adjust the parameters according to the actual situation.

4. Application Case

According to the approach described in this article, we have carried out real-world tests. The setup involves four entities: a public cloud for materials, a research and development institution A, a steel enterprise B, and a large equipment manufacturing plant C. We have built a steel industry chain within the consortium blockchain, created smart contracts of steel data discovery and data proof, and developed a cloud platform that allows users to manage the entire lifecycle of steel materials data, including management, discovery, sharing, traceability, and valuation.

• Data management

Users can store and manage materials data within the system. When the data are submitted, it will trigger the data proof smart contract automatically, ensuring the protection of the data’s intellectual property rights. The new data page, depicted in Figure 8, allows users to input metadata, upload master data, and gain fine-grained permission control. A part of the metadata is filled in by the data owner in the table of basic information and material feature data, while others are automatically generated by the system, including ID, URL, size, time, and other details. The data details page after submission is shown in Figure 9. The data owner and authorized users can view this page and use related applications, such as data details, similar data, data tracing, data valuation, etc.

• Similar material discovery

According to the Materials Data Discovery Protocol, users can find materials data that are similar to their own material in the InterMat network, as depicted in Figure 10. The search results include material name, node name, data type, similarity, and data value. The search results are arranged in descending order based on similarity. Data that are accessible can be viewed directly, while data that require permission can be requested. Through this module, users in this example can discover various data related to 9Ni steel from different organizations, such as experimental data, production line data, calculation data, and standard data.

• Data traceability and valuation

In the InterMat system, data users can request, share, and exchange data, while data owners can expose and modify data. These actions are recorded in the blockchain and are difficult to alter. On the data details page, there are traceability information and data valuation modules, as shown in Figure 11. By clicking the Trace button, three parts of data flow information are displayed. The first part includes basic data information, such as data digest, signature, and public key. The second part contains the traceability information of data modifications, recording the data signature after each modification using Data-chain technology. The third part includes data transmission information, which records the exchange, reference, and other details using Behavior-chain technology. Based on the popularity and evaluation of the data, the system calculates their value according to the model, which is also displayed on this page.

5. Evaluation and Discussion

Verified through practical application cases, the approach in this paper effectively addresses the issues related to data sharing, including sharing technology and driving force. And it possesses the following features:

• Materials data scene adaptation.

In this paper, we start from the characteristics of materials data through sorting out the material metadata, building the data identifier and parsing method, and establishing the standard data set of materials data, and we manage the data standardization of multi-source heterogeneous data. In addition, we quantify the value change of data in the sharing process through the value model, which promoted the sharing of materials data.

• High security of the system.

This approach uploads metadata onto a blockchain, while the master data are stored locally or in a private cloud, thus achieving controllability of the master data. Data version traceability and copyright traceability can be realized through the design of Datachain and Behaviorchain so that data copyright can be well protected.

• Reasonable data sharing incentives.

The system sets up a data valuation model and evaluates data value and node value through interactive data, which promotes the sharing of high-quality data.

• High scalability.

For materials data, through the metadata setting of different materials, the data can be expanded from steel to non-ferrous alloys, polymer materials, and other materials. For the network structure, through the hierarchical design of the nodes, industry chains, and InterMat, multi-organization node data can be added, and the scalability of the data source can be realized.

6. Conclusions

The establishment of an infrastructure for sharing materials data must not only facilitate efficient communication between different entities, but also incorporate effective measures for copyright protection and incentivize data sharing. This article examines the shortcomings of traditional data sharing solutions and proposes a blockchain-based solution for the discovery and sharing of materials data. By leveraging the unique features of blockchain technology, this solution addresses the challenges that traditional approaches struggle to overcome, thereby facilitating the comprehensive collection of multidimensional materials data. InterMat provides an inner product encryption approach for data discovery, which offers a level of privacy protection for the data. However, with the growing volume of shared material data and the need for enhanced privacy protection, it is necessary to investigate additional methods of privacy protection. In the future, on the basis of materials data sharing, we can utilize privacy computing technology to apply various applications for data. In the state of data encryption, we can collaborate, analyze, and model data from different organizations. The integration of blockchain and privacy computing technologies can further safeguard data privacy, establish trust mechanisms for multi-organizational data sharing, and serve as a foundational technology for the next generation of materials data infrastructure.