Electrochemical series for materials makes predicting oxidation states easy
Machine-learning trained model could open up new opportunities in materials discovery
A new electrochemical series should offer scientists a better way to understand and predict oxidation states in materials. The team that created the series with the help of machine learning says it could help to accelerate research in areas such as batteries and materials discovery.
The oxidation states of ions are essential for inferring structure–property relationships in materials. Their values depend on both the positions of nearby species and their electronic chemical potential. ‘Different ions gain or lose electrons – or change oxidation state – at different electronic chemical potentials,’ explains Tim Mueller, who led the study at Toyota Research Institute in the US. He adds that there’s an established list of potentials at which that happens, which is known as the electrochemical series.