Materialism: A Materials Science Podcast cover image

Materialism: A Materials Science Podcast

Episode 71: Automating Materials Discovery

Aug 28, 2023
This podcast explores the advancements in automating materials discovery, the significance of knowledge graphs and training for chemists and material scientists, the challenges and opportunities of requiring extra data for academic papers, the costs of autonomous experiments, and the importance of interdisciplinary teams in approaching unsolved chemistry challenges.
35:38

Podcast summary created with Snipd AI

Quick takeaways

  • Autonomous labs in materials and chemistry research are rapidly advancing, with significant progress in hardware and software over the past five to ten years, showing promise in complex areas like heterogeneous catalysis and water splitting.
  • The success of autonomous labs in materials and chemistry research depends on interdisciplinary collaboration, specialized training to shift the paradigm of experiment design, and the development of software tools like knowledge graphs for experiment automation and data analysis.

Deep dives

The Emergence of Autonomous Labs in Materials and Chemistry

Autonomous labs in materials and chemistry are an emerging and intriguing technology. They involve the use of autonomous robots and automation to conduct experiments and research in these fields. The development of autonomous labs has been an evolutionary process, with advancements in automation occurring over the past two decades. The current state of autonomous labs falls between simplistic laboratory automation and advanced artificial intelligence systems like Jarvis from Iron Man. The field is progressing rapidly, especially in the last five to ten years, with significant advancements in hardware and software. Although there is no specific killer app for autonomous labs in materials or chemistry, applications in multivariate and complex areas like heterogeneous catalysis and water splitting show promise. However, the challenges of training chemists to think in an open-ended and closed-loop experimental fashion pose a significant obstacle. Collaborative teams with expertise in diverse disciplines will be essential in the future of autonomous labs.

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