Enhancing Decision-Making in Materials Development through Materials Modelling

Developing new materials involves navigating a series of complex decisions. For example:

 

• Should the focus be on improving performance or reducing cost?
• How will manufacturing processes impact material properties?
• What trade-offs are acceptable in terms of sustainability or production timelines?

 

Every choice impacts the final material and its applications. Materials modelling, and in particular multiscale materials modelling, aims to improve this process by linking simulations across different scales, providing a detailed understanding of how decisions at the micro or nanoscale affect macroscopic performance. However, to be effective, modelling tools must not only generate accurate predictions but also support practical decision-making [1,2].

 

Requirements for Effective Multiscale Materials Modelling Frameworks

 

For multiscale materials modelling to enhance decision-making, the tools must address key needs [1,2]:

 

• Integrated Modelling Across Scales: Material behaviour and property are often dominated by phenomena occurring at different scales, therefore the system should seamlessly connect models at different scales, such as atomic-level simulations, microstructural analyses, and continuum-scale models. This allows users to trace how changes at one scale—like altering a grain structure—affect performance at another scale, such as fatigue resistance or thermal stability.
• Centralized Data Management: All experimental and simulation data should be stored in a centralized, accessible system. This ensures that users can quickly find, compare, and utilize data to answer key questions, such as: What environmental conditions impact material performance the most? Which processing techniques yield the most consistent results?
• User-Friendly Interfaces: Not all users will be experts in multiscale modelling. The system should provide intuitive interfaces that simplify the complexity of workflows, allowing non-specialists to access results and make decisions confidently without needing deep technical expertise.
• Workflow Automation: Routine tasks, such as sensitivity analyses, optimization, or running parameter studies, should be automated. Automation reduces the time spent on repetitive tasks, ensures consistency, and frees up engineers to focus on interpreting results and exploring solutions.
• Flexibility and Modularity: Materials development relies on many tools and methodologies. A multiscale framework must be flexible enough to integrate existing tools, incorporate new models, and adapt to different use cases. Open, modular designs that use standard APIs can ensure interoperability and scalability.
• Uncertainty Quantification and Risk Assessment: Decision-making often involves balancing risks and uncertainties. Frameworks should provide tools to quantify uncertainties, offering confidence intervals or sensitivity analyses to help users make informed decisions under uncertain conditions.

 

Supporting Better Decisions Through Multiscale Modelling

 

When these requirements are met, multiscale materials modelling frameworks can transform decision-making by providing clear, actionable insights at every stage of materials development:

 

• Concept and Design: Compare and rank material options based on simulated performance metrics.
• Process Optimization: Predict how changes in manufacturing parameters affect material properties.
• Performance Validation: Test virtual prototypes in simulated environments to identify potential weaknesses or trade-offs before physical testing.

 

These capabilities allow teams to focus resources on the most promising solutions, reduce development time, and better manage risks.

 

Integrating Materials Modelling Frameworks in decision-making in NICKEFFECT

 

The goal in NICKEFFECT is to create tools that empower engineers and scientists to make confident, well-informed decisions. These tools must bridge the gap between computational complexity and real-world usability, making advanced simulations accessible to users from diverse technical backgrounds. Within the project Ansys will develop prototypes to integrate reliable data management, user-friendly interfaces, and robust automated multiscale materials modelling workflow to support organizations in navigating the challenges of modern materials development. This is not only about improving technical accuracy but also about enabling smarter, faster, and more sustainable decision-making across industries. To this end Ansys will build on results from previous EU projects [3,4, 5] and internal developments [6].

 

[1] https://ntrs.nasa.gov/citations/20180002010

[2] https://zenodo.org/records/4054009

[3] http://musicode.eu/

[4] https://www.the-force-project.eu/

[5] https://iclimabuilt.eu/

[6] https://www.ansys.com/en-gb/blog/developing-software-framework-to-design-and-optimize-materials