BatteryMat: a hierarchical machine-learning and DFT framework for average-voltage screening of lithium-ion cathode materials

Abstract

Density functional theory (DFT) predicts cathode voltages accurately but does not scale to the combinatorial chemical spaces of modern materials databases, while pure machine-learning surrogates are fast but cannot guarantee thermodynamic consistency. We introduce BatteryMat, a three-tier framework that promotes single-pass average-voltage prediction with the Atomistic Line Graph Neural Network (ALIGNN) as the primary screening signal across JARVIS-DFT, then validates survivors with ALIGNN-FF force-field delithiation profiles and automated PBE+U or optB88-vdW+U supercell DFT. The exchange-correlation functional is selected automatically by spacegroup, and the lithium metal reference is recomputed in the same plane-wave basis as the cathode runs, removing a systematic offset of about 1 V present in tabulated values. Trained on 7,610 ALIGNN-FF delithiation voltages, the ALIGNN predictor reproduces the force-field labels with a mean absolute error of 0.17 V and a coefficient of determination of 0.94; this measures distillation fidelity to the force-field protocol, not agreement with DFT or experiment. On four commercial chemistries (LiFePO4, LiMnPO4, LiMn2O4, LiCoO2) the DFT tier reproduces the experimental average voltage to within 0.3 V and the theoretical volumetric capacity to within 5%; a fifth, non-stoichiometric layered entry is carried as an edge case. The pipeline prioritises, rather than generates, existing structures: it ranks the lithium-containing JARVIS-DFT pool into 71 candidates and a scan of about 4.49 million Alexandria structures into 213, all surrogate-level leads awaiting DFT validation rather than confirmed cathodes. BatteryMat is available at https://github.com/atomgptlab/batterymat with a demo at https://atomgpt.org/battery.