Nanomaterials have a large surface area to volume ratio, increasing the amount of surface interactions with aqueous environments. Upon exposure to aqueous environments, nanomaterials experience cation leeching of surface metals which can be controlled through understanding material properties that govern release favorability. Two nanomaterials of interest to our group are Li(Ni1/3Mn1/3Co1/3)O2 (NMC) and CuO which are used in applications such as Li-ion cathodes and agriculture, respectively. These materials behave differently where cations released from NMC have shown to be toxic while Cu cations that were intended for release can positively influence plant immune response is diseased soils. Through computational models we have thoroughly investigated NMC and other formulations of this complex oxide gaining insight on factors that contribute to metal release favorability such as metal oxidation states, spin ordering of surface metals, and oxygen bonding saturation. Through assigning such descriptors to our metal release values we have constructed a random forest tree (RFT) machine learning (ML) model that has had success in predicting metal release from our cathode surfaces. We seek to increase the complexity for predicting metal release by extending our NMC trained ML model to CuO nanoparticles, introducing metal coordination environments other than octahedron. Using our DFT + Thermodynamics approach to calculate metal release, we model release from CuO (hkl) surfaces (with h, k, and l being 0 or 1) to generate an expansive set of metal release data to be introduced to our ML model. Through this work we aim to extend the predictability of our ML model to other materials beyond the (001) cathode surface.

Training machine learning model to predict metal release beyond (001) Cathode Surfaces: Introduction of cu based nanomaterials

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ACS Fall 2022

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