  Lithium-based disordered rocksalts (LDRs), which are an important class of
cathodes for advanced Li-ion batteries, represent a complex chemical and
configurational space for conventional density functional theory (DFT)-based
high-throughput screening approaches. Notably, atom-centered machine-learned
interatomic potentials (MLIPs) are a promising pathway to accurately model the
potential energy surface of highly-disordered systems, such as LDRs, where the
performance of such MLIPs have not been rigorously explored yet. Here, we
represent a comprehensive evaluation of the accuracy, transferability, and ease
of training of five MLIPs in modelling LDRs, including artificial neural
network potential developed by the atomic energy network (AENET), Gaussian
approximation potential (GAP), spectral neighbor analysis potential (SNAP) and
its quadratic extension (qSNAP), and moment tensor potential (MTP).
Specifically, we generate a DFT-calculated dataset of 10842 disordered
LiTMO$_2$ and TMO$_2$ configurations, where TM = Sc, Ti, V, Cr, Mn, Fe, Co, Ni,
and/or Cu. Importantly, we find AENET to be the best in terms of accuracy and
transferability for energy predictions, while MTP is the best for atomic
forces. While AENET is the fastest to train at low number of epochs, the
training time increases significantly as epochs increase, with a corresponding
reduction in training errors. Note that AENET and GAP tend to overfit in small
datasets, with the extent of overfitting reducing with larger datasets.
Finally, we observe AENET to provide reasonable predictions of average
Li-intercalation voltages in layered, single-TM LiTMO$_2$ frameworks, compared
to DFT ($\sim$10% error on average). Our study should pave the way both for
discovering novel LDR electrodes and for modelling other configurationally
complex systems, such as high-entropy ceramics and alloys.
