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Intra-atomic supervision is crucial for achieving accurate energy decomposition in machine-learning interatomic potentials, revealing hidden allocation failures.
Charge conditioning in ML force fields can drastically enhance predictive accuracy while maintaining computational efficiency, achieving remarkable reductions in error metrics with minimal data.
