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Merging Quantum Chemistry and Machine Learning for More Accurate Computational Models

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If you have a question about this talk, please contact Dr Philipp Pracht .

Machine learning methods offer the promise of rapid prediction of molecular properties. In many cases, traditional computational chemical methods face a tradeoff between accuracy and speed. Our efforts seek to use inexpensive approximate quantum chemical methods as components of ML models to predict complex properties, including aqueous pKa and organic solar cell performance. The quantum chemical descriptors are calibrated with experimental data through the ML model.

This talk is part of the Lennard-Jones Centre series.

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