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Coles Data-driven Discovery in the Chemical Sciences: Faraday Discussion 256 at Bayshore Shopping Centre in Ottawa, ON
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Big data, machine learning and artificial intelligence are becoming increasingly more central in the chemical sciences. It is important to consider how data drives new discoveries in chemistry at present and how data-driven discovery may develop in the future.
The Faraday Discussion is an excellent format to facilitate in-depth, interdisciplinary discussions between academic and industrial scientists from both molecular and materials fields. This volume offers new insights on how data-driven discovery can advance the chemical sciences.
Bringing together scientists from a wide range of chemical science disciplines to deliberate and examine the ongoing role of data-driven discovery in their fields, this volume discusses both recent developments and future possibilities.
In this volume, the topics covered are organised into the following themes:
Discovering chemical structure
Discovering structure–property correlations
Discovering trends in big data
Discovering synthesis targets
Big data, machine learning and artificial intelligence are becoming increasingly more central in the chemical sciences. It is important to consider how data drives new discoveries in chemistry at present and how data-driven discovery may develop in the future.
The Faraday Discussion is an excellent format to facilitate in-depth, interdisciplinary discussions between academic and industrial scientists from both molecular and materials fields. This volume offers new insights on how data-driven discovery can advance the chemical sciences.
Bringing together scientists from a wide range of chemical science disciplines to deliberate and examine the ongoing role of data-driven discovery in their fields, this volume discusses both recent developments and future possibilities.
In this volume, the topics covered are organised into the following themes:
Discovering chemical structure
Discovering structure–property correlations
Discovering trends in big data
Discovering synthesis targets
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