Uncertainty quantification and propagation in atomistic machine learning
Jin Dai,
Santosh Adhikari,
Mingjian Wen
Abstract:Machine learning (ML) offers promising new approaches to tackle complex problems and has been increasingly adopted in chemical and materials sciences. In general, ML models employ generic mathematical functions and attempt to learn essential physics and chemistry from large amounts of data. The reliability of predictions, however, is often not guaranteed, particularly for out-of-distribution data, due to the limited physical or chemical principles in the functional form. Therefore, it is critical to quantify t… Show more
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