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Prediction of transition state structures of gas-phase chemical reactions via machine learning

Chemistry

Prediction of transition state structures of gas-phase chemical reactions via machine learning

S. Choi

This groundbreaking study by Sunghwan Choi unveils a machine learning model that predicts transition state structures in organic reactions with remarkable accuracy. Achieving a 93.8% success rate, this innovative approach enhances our understanding of chemical reaction mechanisms.

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