Interpretable machine learning for critical evaluation of scientific ML models - the case of reaction prediction

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• David Peter Kovacs
• Monday 26 October 2020, 17:00-17:30
• virtual ZOOM meeting ID: 263 591 6003, Passcode: 000042, https://us02web.zoom.us/j/2635916003?pwd=ZlBEQnRENGwxNmJGMENGMWxjak5nUT09.

If you have a question about this talk, please contact Bingqing Cheng .

In this talk, I will present an approach for interpreting ML models and will illustrate it through the example of the Molecular Transformer, the state-of-the-art model for reaction prediction. I will outline a framework to attribute predicted reaction outcomes both to specific parts of reactants, and to reactions in the training set. Furthermore, I will demonstrate how to retrieve evidence for predicted reaction outcomes, and understand counterintuitive predictions by scrutinising the data. Additionally, I will point out ”Clever Hans” predictions where the correct prediction is reached for the wrong reason due to dataset bias. Finally I will illustrate how the reported accuracy of models can be much higher than it is in reality due to not appropriate train-test splitting. For further details see: https://chemrxiv.org/articles/preprint/Quantitative_Interpretation_Explains_Machine_Learning_Models_for_Chemical_Reaction_Prediction_and_Uncovers_Bias/13061402

This talk is part of the Machine learning in Physics, Chemistry and Materials discussion group (MLDG) series.

This talk is included in these lists:

• Hanchen DaDaDash
• Lennard-Jones Centre external
• Machine learning in Physics, Chemistry and Materials discussion group (MLDG)
• virtual ZOOM meeting ID: 263 591 6003, Passcode: 000042, https://us02web.zoom.us/j/2635916003?pwd=ZlBEQnRENGwxNmJGMENGMWxjak5nUT09

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