What is Materials Informatics?

Materials informatics is the application of computer science, data science, and artificial intelligence to the characterization, categorization, selection, and development of materials.

Before the introduction of materials informatics, engineers and scientists created lists of materials and store that information in handbooks or simple databases. When they needed information about a specific material, they would look it up manually.

The application of informatics offers improved methods for gathering and organizing material property data, tracking the history and use of that data, streamlining and automating the search for materials, and even aiding in the development of new materials. Materials science across your entire organization benefits when data science, computer science, and artificial intelligence (AI) are applied to a materials informatics solution. 

Early in the Industrial Revolution, engineers learned that understanding and documenting the characteristics of different materials was an essential part of product development. When deciding what materials to use in a machine, engineers needed to know the density, stiffness, strength, and cost of each material they wanted to use. Over time, listing material properties became standardized, making data sharing possible.

Before computers, this information was captured in data sheets or handbooks, and engineers would look up properties manually, summarize them in tables, and make their material choices accordingly. More seasoned engineers relied on prior knowledge and preferred materials they had used before. When computers were introduced, these lists were digitized into searchable databases, but the process remained manual.

As the number of available materials grew and the area of materials science and engineering matured, people began to apply modern informatics technology to their material data, leading to the development of materials informatics.

Some of the key technologies made available in recent years to researchers, engineers, and materials manufacturers include:

• Computational algorithms
• Data analytics
• Graphical material selection tools
• Natural language search
• Intuitive user interfaces
• Machine learning (ML)
• Redundant and secure data storage
• Methods for systematic material comparison

Instead of relying on trial and error or prior knowledge to conduct materials searches, engineers now have access to the latest methodologies for search, optimization, and analysis for materials discovery and materials development. People without specific domain knowledge in materials science can start with a design goal and use material informatics to identify the right materials for their application. 

The workflow and functionality of any materials informatics system can be divided into two parts. The first involves how datasets are obtained and structured. The second focuses on using the system for materials discovery or materials design. Even if you are purchasing a ready-to-use system, it is important to understand how it was created to ensure the materials informatics systems tools take full advantage of current capabilities and can easily incorporate the next generation of advances in materials science and informatics.

6 Key Aspects of a Materials Informatics Tool

Before a material informatics system can be used for materials research, it must be populated with datasets that describe the materials it manages. This data must also be organized in a way that enables consistent access. Key aspects of any materials informatics tool include:

1. Datasets

The most crucial component in an effective tool for materials investigation and selection is the dataset representing each material and its variations. When available, data can be purchased, obtained from government or academic sources, or provided by material suppliers. Newer materials informatics platforms can leverage large language models (LLMs) to extract and digitize legacy data stored in data sheets, lab reports, handbooks, and older databases into usable datasets. When data is unavailable or incomplete, especially for new or advanced materials, an organization must conduct testing to obtain experimental data or use existing information from similar materials to calculate or estimate missing properties.

2. Data Structure

How the datasets are structured is the first step in turning a material database into a tool that leverages data analytics, computation, and artificial intelligence. The chosen data structure must support tools for entering, managing, searching, and accessing data, both within the system and through external tools.

Key considerations that the data structure must support include:

• Units
• Standard properties
• Statistical variations
• Material pre- and post-processing
• Traceability
• Interlinking between related records

3. User Interface

Ultimately, the goal of a materials informatics system is to help users store, manage, understand, choose, or develop materials. Therefore, the user interface is key to the usefulness of any platform. It must be intuitive while remaining powerful enough to handle complex materials trade studies.

For example, it should provide experienced materials R&D engineers with the features needed to explore families of advanced composites for a deep-space probe while still being usable by a product development engineer comparing polymers for a consumer product.

A strong user interface also includes intelligent search tools that allow users to filter by properties and identify related materials. Visualization tools enable users to compare differences between materials or variations in material data.

One of the most common tools for systematic material selection is an Ashby Plot. This scatter plot displays two or more material properties in a way that enables engineers to compare characteristics and make data-driven decisions about materials quickly.

4. Cross-Platform Integration

The data retrieved from a materials informatics system must be easily transferable to other tools that engineers and researchers use within their native user interfaces (UIs), as far as practical. A platform should be able to directly connect to computer-aided design (CAD), simulation, manufacturing, supply chain, and quality assurance tools.

This can be achieved through:

• A published application programming interface (API)
• Exporting standard files
• Direct connections to common software tools used across the materials industry

A properly constructed cross-platform integration provides effortless access to a single source of truth and information on materials for design, manufacturing, and simulation engineers across an organization.

5. Analytic and Predictive Tools

Robust data structures combined with large, high-quality datasets enable data analysis for comparison, ranking, optimization, and the application of machine learning techniques such as deep learning and predictive analytics. Simulation can also support chemical analysis, metamaterial design, and predictive modeling. This is especially useful when a materials informatics system is used for a project focused on new material synthesis.

6. Traceability

One advantage of applying information science to materials science is the the ability to achieve robust traceability throughout the entire process. From tracking the standards used for material testing to recording who added or modified what data and when, traceability adds an extra level of security and accountability to the datasets in the system.

User Workflows in a Materials Informatics System

Users of materials informatics systems generally follow three types of workflows: selecting a material, finding data on a known material, and managing material data.

When an engineer or scientist wants to select a material, they generally follow three steps:

1. Define Requirements for the Material

The starting point for any data-driven exploration of material options is establishing the requirements for that material. Users must understand how their materials informatics system defines those requirements or which material characteristics influence them. Requirements are not just mechanical properties like density or stiffness. They also include factors such as cost, availability, required treatments, handling, sustainability goals, and regulatory restrictions.

2. Select the Material

The user then uses the system’s capabilities to explore, search, compare, and predict to select the material that best meets their requirements. Because many requirements can be conflicting, a simple search by required value ranges rarely works. Leveraging big-data capabilities in a materials informatics system enables engineers find the best material fit through a data-driven materials selection process.

3. Validate and Track the Selected Material

Once a material is selected, the choice must be verified through testing or simulation. This can be done with simple calculations within the materials informatics system or through simulation that uses data derived from it. In cases in which virtual testing through simulation is not practical, the user must perform physical testing. The results of any additional testing should then be entered back into the system to enhance machine learning models. Users should also track how, when, and where they use material data from the materials informatics platform for quality and traceability purposes.

When a user wants to access data on a known material, they generally search the system's dataset using an identifier like a name, standard number, or internal nomenclature. Once found, they then extract the information in the preferred format for easy use. The extracted data files should also contain some information on the data source.

The final workflow is among the most important: managing material data. This process involves locating relevant data records and editing them in an intuitive and traceable manner. It can also include steps to link related data and flag inaccurate or replaced data as unusable. 

Material informatics has made significant strides from simple databases to the current AI-enabled, integrated tool suites. Organizations of all sizes can realize the advantages of materials informatics by staying up to date on the latest trends and adopting the right materials intelligence tool for their needs.

Current Trends in Materials Intelligence Tools

The most significant trend for material Informatics is the continued usage of machine learning and deep learning (DL) frameworks. These technologies, along with large language models, are making AI-driven materials discovery a reality that can deliver significant advantages to users.

Another emerging trend is better integration, both within materials information platforms and between these platforms and other applications like supply chain tools, CAD software, and simulation. More advanced enterprises are also utilizing AI agents to automate tasks related to material data maintenance and discovery, as well as integration into their enterprise workflow.

Data science teams and companies developing materials informatics tools are also taking advantage of greater access to material data, cloud-based technology stacks, and more sophisticated visualization and comparison tools. Together, these trends are driving more accurate, efficient, and innovative uses of material data.

Implement the Right Materials Informatics Tools

Engineering teams can improve their material data management and selection processes with tools like the Ansys Granta materials information, selection, and data management product collection, which delivers data-driven materials intelligence to users ranging from design engineers to materials scientists. These tools enable organizations to capture and safeguard material data while supporting material evaluation and selection.

For companies that require a comprehensive materials information system, a solution like Ansys Granta MI Enterprise materials data management software supports integration with CAD, computer-aided engineering (CAE), and product life cycle management (PLM) tools. Design teams benefit from intuitive user interfaces and tracking the single source of truth across the enterprise. For organizations mainly interested in the material selection aspect of materials informatics, specialized tools like Ansys Granta Selector materials selection software can help users make informed decisions to innovate, resolve materials challenges, reduce costs, and validate material choices.

Companies implementing material informatics should also build internal expertise, or collaborate with a partner, to characterize and capture new materials, share materials intelligence, and develop best practices for materials science and the use of materials data in downstream applications. An industry-recognized standard for this type of partnership is the Ansys Granta  collaboration team. 

Materials informatics moves beyond data management and analytics by integrating simulation. Simulation employs computational methods to support all three phases of the user workflow: defining, selecting, and developing materials. Because material properties are fundamental inputs of any simulation, engineers can conduct analyses to determine which properties are needed, develop new material combinations or composite configurations, calculate the effects of post-processing steps, and verify the effectiveness of a material, all without costly physical testing.

The combination of Ansys Mechanical structural finite element analysis software and the Ansys Granta Materials Data for Simulation properties database, an optional material data library available with key Ansys solvers, is a powerful example of how materials informatics can be integrated directly into the simulation process, and vice versa. Engineers can choose materials and evaluate their performance under a range of load conditions. An optimization module and design-of-experiments interface, such as the design exploration tools built into Mechanical software, can also help engineers assess design sensitivity to material property variations and even optimize material requirements for a design. 

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