AI for Materials Science Tutorial at IJCAI 2019

Developing new materials is an expensive and time-consuming process. AI can help to speed it up through powerful optimization methods and machine learning models that approximate the expensive processes involved. This tutorial will give an overview of applications of AI in materials science, highlighting challenges and opportunities for AI research.

Description

Advanced materials and manufacturing methods are important drivers of today’s economy. They have made consumer devices like TVs and computers ubiquitous by enabling cheap mass production of reliable products and enabled industrial applications on a scale not seen before. There is a need for new materials in high-performance environments, for example in optoelectronics for computer and cellular technology, photovoltaics and consumer electronics, as well as for extreme environments in space and aviation applications. However, designing new materials is a slow and laborious process, requiring significant investments in capital and labor. Materials synthesis, development, and characterization are time-intensive and expensive processes; in particular as a large number of different alternatives have to be evaluated and optimized over. Often, there is little guidance from theoretical models, and synthesizing new materials to be tested is only possible on a small scale.

While models based on a small number of empirical observations have been used to speed up this process for decades, recent advances in AI and machine learning have ushered in a new era of effective and efficient modeling and exploration of large spaces of possible designs with complex performance landscapes. Powerful optimization techniques that make no assumptions about properties of the underlying process are able to quickly narrow in on promising designs. Process models induced by machine learning allow to approximate complex relationships reliably and predict properties of hypothetical materials. Despite a growing number of successful applications and large interest from domain scientists, the AI community has paid little interest to these important applications. This tutorial will provide an introduction and overview of this application area, and highlight challenges and opportunities for AI research.

Tentative Outline

  • Introduction and motivation -- why is design and discovery of new materials hard, examples of the impact optimized materials can have
  • History -- surrogate modeling for engineering design
  • AI background -- brief overview of applicable optimization and machine learning techniques
  • Optimization to efficiently discover materials with desired properties
  • Machine Learning to approximate expensive processes and predict materials
  • Brief overview of applications of other AI methods
  • Example -- PhaseMapper system
  • Example -- surrogate-model-based optimization of laser-induced graphene production
  • Challenges and Opportunities -- real-time processing of large amounts of data, interpretation of machine learning models to inform new theories, integration into manufacturing processes
  • Conclusion -- summary, pointers to further reading

Materials

Coming soon.