ANSYS BLOG
November 25, 2019
Understanding Machine Learning for Materials Science Technology
When materials science and engineering (MSE) specialists study substances at the molecular level, they are better able to alter their mechanical properties.
Using electron microscopy and other techniques, they have been able to visualize single atoms and tailor materials to meet market demands. However, demand is growing at a rate that outpaces traditional MSE development tools.
Machine learning dramatically decreases the time it takes to develop stronger, lighter materials. This is important to the automotive, aerospace and construction sectors.
To address this demand, engineers can combine machine learning and materials science technologies to investigate how to optimize mechanical properties.
What is Machine Learning?
As computers get faster and storage gets larger, the ability to collect and assess big data sets increases.
Engineers need tools to process big data and create AI systems.
Machine learning has become an integral tool for scientists studying big data. It uses various artificial intelligence (AI) algorithms to process complex datasets. It then uses the data to train digital neural nets to predict various scenarios and make decisions.
The predictions can help engineers and scientists understand various systems outside of materials science.
For instance, machine learning has been used to train autonomous vehicles and improve prognostics in the oil and gas industry.
Machine Learning and Materials Science
MSE is a field that relies heavily on experiments to understand and predict material behavior.
The challenge is that there are many environments that are hard — or impossible — to replicate, such as:
- Nuclear reactors
- Space
- Atomic scale
In these situations, MSE specialists can use machine learning for materials science to analyze existing property or atomic structure data. The AI system can then model, or predict, how a material would react to an environment.
Simulation improves the understanding of material properties, including atomic behavior, that is hard to probe experimentally.
This technique can also be used alongside experimentation. For instance, the AI can predict new thermodynamically stable materials. Then, MSE specialists can move on to experimental testing. This is more efficient than trial and error, allowing advancements to be made more quickly.
Machine learning and materials simulation techniques are starting to become integral components of MSE curricula and industrial applications. In fact, the International Materials Education Symposium (IMES) has chosen it as one of the themes for IMES 2020. To join the discussion, register here. Early bird deadline is Dec. 20, 2019.
To learn more about materials science and simulation, read: Material Intelligence with Ansys Granta.
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