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Accelerating Scientific Discovery with Deep Learning

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RCLW03 - Accelerating statistical inference and experimental design with machine learning

Scientific discovery is one of the most promising application areas for AI and machine learning. Learned emulators, trained on simulated data, are already accelerating scientific modelling by multiple orders of magnitude across widely differing domains. This talk will give an introductory overview of scientific emulation and will be illustrated with examples from weather forecasting, drug discovery, materials design, and quantum chemistry.

This talk is part of the Isaac Newton Institute Seminar Series series.

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Branching Out Talk Series Department of Chemistry CCLS 2020

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