Part 2: new horizons and data-driven innovations
In the first part of our blog series “AI in MatWerk”, we presented the basics and three central fields of application of artificial intelligence in materials science. Now we take a look at further innovative applications and shed light on the data-driven technologies that have the potential to take materials engineering to a new level.
- Surrogate Modeling
In general, we understand a surrogate model in engineering to be a technique used when an event cannot be readily and directly evaluated and a model of the event is used instead. From an artificial intelligence perspective, surrogate models are approximation models that make complex, time-consuming simulations more efficient through machine learning (#ML). Models can approximate simulations such as Molecular Dynamics or Density Functional Theory to predict material properties in real time. They are used in process monitoring and enable predictions with high accuracy and less computational effort.
- Materials Discovery & Design
The development of new materials has often been a lengthy process that relies heavily on experimentation, loose design rules and sometimes even chance. AI is fundamentally changing this approach through concepts such as:
- Inverse Design: AI develops materials based on desired properties.
- Bayesian Optimization: Efficient optimization of compositions with minimal experimentation.
- Virtual Screening: Virtual evaluation of millions of materials to identify the most promising candidates for physical testing.
This not only speeds up the discovery process, but also significantly reduces the number of experiments required.
- Large Language Models
Large language models (#LLMs) such as #GPT offer new possibilities, from automated literature searches to support in material design. Despite the potential of such models, caution is advised as they can be prone to hallucination. Nevertheless, they are valuable tools to increase efficiency and precision in #research.
Upcoming topics
Data is the basis for AI-powered innovation. In the upcoming posts, we will highlight how high-quality data is generated, annotated, and used to advance materials science.
Visit our blog regularly to learn more or take advantage of our training opportunities to discover AI applications for your work. We look forward to your feedback and discussions!
