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Alán Aspuru-Guzik: "Billions and Billions of Molecules"
Alán Aspuru-Guzik discusses a machine learning approach to materials discovery, addressing the challenges of energy storage and sustainable materials, and highlighting the need for collaboration and innovative solutions.
- Alán Aspuru-Guzik discusses the need for a new approach to materials science, particularly in the context of energy storage and organic electronics.
- He highlights the challenges of generating large amounts of data and the need for efficient screening methods.
- Aspuru-Guzik presents a machine learning approach to materials discovery, using neural networks and Gaussian processes to predict the properties of molecules.
- He discusses the importance of collaboration between experimentalists and theorists, as well as the need for cheap and efficient methods for generating large numbers of molecules.
- Aspuru-Guzik highlights the potential of flow batteries for large-scale energy storage and the need for innovative solutions to address the energy crisis.
- He mentions the importance of developing materials that are cheap, efficient, and sustainable, and the need for a global effort to address the challenge of energy storage.
- Aspuru-Guzik discusses the potential of machine learning to accelerate materials discovery and the need for creative thinking and collaboration to address complex problems.
- He highlights the success of his team in generating large numbers of molecules and scoring their properties, using a combination of machine learning and experimental data.
- Aspuru-Guzik mentions the importance of developing new approaches to energy storage and the need for innovative solutions to address the energy crisis.
- He discusses the potential of quantum computing to accelerate materials discovery and the need for collaboration between industry, academia, and government to address the challenge of energy storage.
- Aspuru-Guzik highlights the importance of sustainability and the need for materials that are cheap, efficient, and sustainable.
- He mentions the potential of machine learning to learn patterns and relationships in large datasets and the need for creative thinking and collaboration to address complex problems.