Speaker
Description
Over the years, X-ray absorption spectroscopy is playing an increasingly important role in the development and innovations in battery science, due to its unique ability to provide accurate information on the electronic structure of redox active elements and local structural information, also in operando conditions. Despite its potential innovative outcomes, the generation of significant developments seems hindered by the resources needed to fully and quickly exploit these capabilities and the limited cooperation between academic and industrial parties. Several challenges can be identified for the development of more performing and sustainable batteries. In this work we utilize a set of supervised and unsupervised ML algorithms including convolutional neural networks (CNNs), random forests, extra trees paired with principal component analysis (PCA) to disentangle structural and electronic parameters and to unambiguously identify transition metal oxidation state and spin variations in layered transition metal cathode materials. Complementary automated fitting approach of big in operando XAS data sets allows us to extract spectral and structural descriptors such as absorber-ligand distances, the energy positions of the rising edge, the pre-edge features, the most influenced spectral points (maxima and minima). To establish structure–property relationships, the extracted descriptors are further correlated with electrochemical parameters. In particular, we show how data driven ML analysis of in operando quasi-simultaneous Mn and Ni K-edges XANES spectra can offer a scalable pathway for real-time monitoring and interpretation of TM-based (TM = Ni, Mn) cathode materials under in operando conditions, paving the way toward data-driven design and optimization of energy-related systems.
