Towards Ultra Low Cobalt Cathodes: A High Fidelity Computational Phase Search of Layered Li-Ni-Mn-Co Oxides

Abstract

Layered Li(Ni,Mn,Co,)O2 (NMC) presents an intriguing ternary alloy design space for the optimization of performance as a cathode material in Li-ion batteries. In the case of NMC, however, only a select few proportions of transition metal cations have been attempted and even fewer show promise. Recently, due to cost and resource limitations of Co, high Ni-containing NMC alloys have gained enormous attention. Here, we present a high fidelity computational search of the ternary phase diagram with an emphasis on high-Ni containing compositional phases. This is done through the use of density functional theory training data fed into a reduced order model Hamiltonian that accounts for effective electronic and spin interactions of neighboring transition metal atoms at various lengths in a background of fixed lithium and oxygen atoms. This model can then be solved to include finite temperature thermodynamics into a convex hull analysis. We also provide a method to propagate the uncertainty at every level of the analysis to the final prediction of thermodynamically favorable compositional phases thus providing a quantitative measure of confidence for each prediction made. Due to the complexity of the three component system, as well as the intrinsic error of density functional theory, we argue that this propagation of uncertainty, particularly the uncertainty due to exchange-correlation functional choice is necessary to have reliable and interpretable results. With our final result, we recover the prediction of already known phases such as LiNi0.33Mn0.33Co0.33O2 (111) and LiNi0.8Mn0.1Co0.1O2 (811) in exact proportion while finding other proportions very close to the experimentally claimed LiNi0.6Mn0.2Co0.2O2 (622) and LiNi0.5Mn0.3Co0.2O2 (532) phases, and overall predict a total of 37 phases with reasonable confidence and 69 more phases with a lower level of confidence. Through our analysis, we also can identify the phases with the highest average operational voltage at a given Co composition. Our method presents a framework that can be extended to searches for other high Ni cathode materials by substituting other transition metal atoms into the lattice such as aluminum and magnesium, which have already shown promise. arXiv:1805.08171v1 [cond-mat.mtrl-sci]

How Machine Learning Will Revolutionize Electrochemical Sciences

“…For Li intercalation materials such as NMC, the thermodynamic energy storage response is prescribed as voltage for different extents of intercalated Li. 24 Density Function Theory (DFT) calculations can, in principle, provide this information. However, the task becomes computationally prohibitive if one wishes to compute the open-circuit voltage for all possible combinations of Ni, Mn, and Co contents over multiple Li intercalation states.…”

“… 31 Additionally, these techniques have been shown to accurately and efficiently expand to many-component systems, 32 enabling design searches that were not possible previously. In a recent work, featurization using atom-centered symmetry functions and neural network as the regressor are used to generate the voltage profile and lattice structure dynamics as a function of Li intercalation states for any arbitrary NMC composition, marking the first step toward a computationally feasible optimization workflow for relevant performance properties of cathode material 24 and anode materials. 33 The ML potentials are seeing incredible progress 34 toward increasing the generalizability, extrapolation capabilities, and principled selection of feautrization and hyperparameters.…”

“…For Li intercalation materials such as NMC, the thermodynamic energy storage response is prescribed as voltage for different extents of intercalated Li . Density Function Theory (DFT) calculations can, in principle, provide this information.…”

$\mathcal{P}^2$: Combining pressure and electrochemistry to synthesize superhydrides

“…To identify the range of stable structures and stoichiometries possible, we initially focused on results obtained at zero pressure and megabar (150 GPa) pressures. The Bayesian error estimation functional with van der Waals correlation (BEEF-vdW) was employed to provide a confidence value (c-value) for compet-ing phases to avoid possible bias due to selection of a particular DFT functional 12 , an approach that has been successfully applied to calculate uncertainty in phase diagrams for other systems 13,14 . To enhance robust assessment of the ground state within the structure search, we used the ensemble of functionals within the BEEF formulation to identify the predicted ground state.…”

Uncertainty Quantification of First Principles Computational Phase Diagram Predictions of Li-Si System Via Bayesian Sampling

“…5 Quantifying the uncertainty is important as this could lead to vastly different conclusions on the identified stable phases and the associated thermodynamics. 6 The challenge associated with systematic uncertainty quantification and propagation through a model has lim-ited the application of these methods to calculation of phase diagrams (CALPHAD). There have been approaches proposed for uncertainty quantification within CALPHAD.…”