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Owing to the trade-off between the accuracy and efficiency,machine-learning-potentials (MLPs) have been widely applied in the batterymaterials science, enabling atomic-level dynamics description for variouscritical processes. However, the challenge arises when dealing with complextransition metal (TM) oxide cathode materials, as multiple possibilities ofd-orbital electrons localization often lead to convergence to different spinstates (or equivalently local minimums with respect to the spin configurations)after ab initio self-consistent-field calculations, which causes a significantobstacle for training MLPs of cathode materials. In this work, we introduce asolution by incorporating an additional feature - atomic spins - into thedescriptor, based on the pristine deep potential (DP) model, to address theabove issue by distinguishing different spin states of TM ions. We demonstratethat our proposed scheme provides accurate descriptions for the potentialenergies of a variety of representative cathode materials, including thetraditional Li$_x$TMO$_2$ (TM=Ni, Co, Mn, $x$=0.5 and 1.0), Li-Ni anti-sites inLi$_x$NiO$_2$ ($x$=0.5 and 1.0), cobalt-free high-nickelLi$_x$Ni$_{1.5}$Mn$_{0.5}$O$_4$ ($x$=1.5 and 0.5), and even a ternary cathodematerial Li$_x$Ni$_{1/3}$Co$_{1/3}$Mn$_{1/3}$O$_2$ ($x$=1.0 and 0.67). Wehighlight that our approach allows the utilization of all ab initio results asa training dataset, regardless of the system being in a spin ground state ornot. Overall, our proposed approach paves the way for efficiently training MLPsfor complex TM oxide cathode materials.

A Spin-dependent Machine Learning Framework for Transition Metal Oxide Battery Cathode Materials