From the President

Welcome to the April 2023 edition of Materials Australia Magazine. For this month’s presidents message I thought it would be a great opportunity to look at the emerging topic of the use of artificial intelligence (AI) in materials science and engineering.

Artificial Intelligence (AI) is a rapidly growing field that has the potential to revolutionize many areas of research and development, including materials science and engineering. In recent years, there has been a significant increase in the use of AI in the physical sciences, with researchers utilizing AI techniques such as machine learning and neural networks to develop new materials and optimize their properties. The application of AI in materials science and engineering has opened up new avenues for research, development, and innovation, with the potential to improve existing materials and create entirely new ones. The use of AI in materials science and engineering involves the application of machine learning algorithms and other AI techniques to large datasets of materials properties, structures, and behaviours. By using these AI algorithms, researchers can quickly analyse large datasets and identify patterns and correlations that would be difficult or impossible to detect using traditional methods. Data can be generated through a variety of sources, including experimental measurements, simulations, and theoretical calculations, and, as a result, optimization of materials and the methods through which to research them, becomes significantly more rapid. AI can also be used to improve the efficiency of manufacturing processes. For example, AI algorithms can be used to analyse the performance of different manufacturing processes and identify areas for optimization. This can lead to improvements in efficiency, reducing costs and improving the quality of the final product.

Benefits of AI in Materials Science and Engineering

The use of AI in materials science and engineering has several benefits, including:

Accelerating Materials Discovery: The use of AI can help researchers to accelerate the discovery of new materials by predicting their properties, behaviours, and structures before they are synthesized or experimentally characterized. This can save time and resources and enable researchers to focus on the most promising candidates.

Optimizing Existing Materials: AI can also be used to optimize the properties of existing materials by predicting how they will behave under different conditions and identifying ways to improve their performance.

Reduced Experimental Costs: By using AI to generate predictive models, researchers can reduce the need for costly experimental measurements and simulations, as the models can be used to predict materials properties and behaviours accurately.

Enabling Multiscale Materials Design: AI can help researchers to design materials at different length scales, from atoms to bulk materials, and across different materials classes, from metals to polymers to ceramics.

Challenges of AI in Materials Science and Engineering

Despite its potential benefits, the use of AI in materials science and engineering also presents several challenges, These include:

Data Quality: The accuracy and completeness of the datasets used to train AI models can have a significant impact on the predictive power of the models. Therefore, researchers must ensure that their datasets are high quality and representative of the materials they wish to study. As may be appreciated the risk of encountering data bias as the result of poor or minimal results may produce conclusions that may appear convincing, but are not necessarily correct.

Interpretability: The black-box nature of some AI models can make it challenging to interpret the predictions they generate. This can be a significant difficulty for researchers who need to understand the underlying mechanisms driving materials properties and behaviours.

Computational Complexity: AI models can be computationally intensive and require large amounts of processing power and memory. This can limit the scalability of AI approaches and make them challenging to apply to large and complex materials systems.

Human Expertise: AI models require human expertise to design and interpret accurately. Therefore, researchers must have a deep understanding of both the AI techniques and the materials science and engineering principles they are applying them to.

Future Prospects

The use of AI in materials science and engineering is still in its early stages, and there is significant potential for further development and innovation. Some of the key future prospects for AI in materials science and engineering include:

Incorporating Physics-Based Models: Combining AI techniques with physicsbased models can help researchers to generate more interpretable and accurate predictive models. These

MATERIALS AUSTRALIA CONFERENCE APICAM2023

21st - 23rd June 2023

The University of Sydney hybrid models can also help to address some of the limitations of blackbox AI models, such as their lack of interpretability.

Multimodal Datasets: By combining data from different sources, such as experimental measurements and simulations, researchers can generate more comprehensive and representative datasets that can be used to train AI models more effectively.

Automated Experimentation: The use of AI can enable the development of automated experimentation platforms that can plan, execute and analyse experiments minimizing the need for manual intervention and eliminating the risk of human error. The goal of automated experimentation is to speed up the experimental process, reduce costs, and improve the accuracy and reproducibility of scientific research.

Conclusions

As AI continues to evolve and improve, it is likely that its use in materials science and engineering will only continue to grow. Researchers will need to work together to address the challenges and limitations of AI in this field, while also exploring new and innovative applications of this powerful technology. With continued

MATERIALS AUSTRALIA CONFERENCE LMT2023

9th -12th July 2023

RMIT University

Materials Australia National Office PO Box 19

Parkville Victoria 3052 Australia

T: +61 3 9326 7266

E: imea@materialsaustralia.com.au

W: www.materialsaustralia.com.au

NATIONAL PRESIDENT Roger Lumley research and development, AI has the potential to transform materials science and engineering and unlock new possibilities for innovation and discovery.

PostScript

I am sure many of you would already have guessed by now that the majority of this article (i.e. >95%) was generated by an artificial intelligence platform (ChatGPT). In this case I presented one or two questions to the AI and was able to generate multiple articles in under 15 seconds each, from which I chose the one I liked the best and completed final editing. The entire process of writing this presidents message took less than 20 minutes. This is an incredibly interesting topic that has implications to the way we teach, learn and prepare written information. There are many and varied ethical questions related to how scientists and engineers are using, or will use AI. As a professional community we need to begin the discussion about these kinds of topics, since they are going to enormously impact our careers and work lives over the next 10-20 years and beyond. What do you think?

Best Regards

Roger Lumley

National President Materials Australia

MATERIALS AUSTRALIA CONFERENCE MISE2023

29th - 31st October 2023

The University of Queensland

This magazine is the official journal of Materials Australia and is distributed to members and interested parties throughout Australia and internationally.

Materials Australia welcomes editorial contributions from interested parties, however it does not accept responsibility for the content of those contributions, and the views contained therein are not necessarily those of Materials Australia.

Materials Australia does not accept responsibility for any claims made by advertisers. All communication should be directed to Materials Australia.