Uncertainty metric builds confidence in machine learned-chemistry
Calculation will help users recognise when they need to retrain a neural network
Researchers in the US have developed a better way to measure the confidence of neural networks’ predictions. ‘When we want to go out and discover new materials, one of the biggest challenges is how do we know if we trust the model or not,’ says Heather Kulik from the Massachusetts Institute of Technology, who led the research. ‘This is something that extends across challenges in chemical discovery, which was the focus of our work, to other areas, such as image recognition or other applications of machine learning.’