Energetically Anchored Machine-Learning Interatomic Potential Embeddings for Reliable Hydrogen Evolution Electrocatalyst Prediction

Reliable prediction of hydrogen adsorption free energy (ΔGH) is essential for accelerating electrocatalyst discovery for the alkaline hydrogen evolution reaction (HER), yet practical machine-learning workflows remain limited by inconsistent energetic definitions, heterogeneous density functional theory (DFT) protocols, and poor out-of-distribution (OOD) generalization. Here, we present an energetically anchored machine-learning framework that integrates machine-learning interatomic potential (MLIP)-derived energetic descriptors with pretrained Crystal Hamiltonian Graph Neural Network (CHGNet) latent embeddings to predict DFT-defined hydrogen adsorption energetics across chemically diverse catalyst surfaces. The framework employs protocol-consistent single-point MLIP energy evaluation on reference geometries to construct thermodynamically aligned energetic descriptors while combining them with structural representations through gradient-boosting regression. Using curated data sets from Catalysis Hub and AQCat25 containing single-, bi-, and trimetallic adsorption systems, the embedding-augmented energetic model achieved high predictive accuracy (R2 = 0.976, MAE = 0.054 eV, RMSE = 0.105 eV) under site-level data splitting, substantially outperforming embedding-only and physicochemical-descriptor-only models. Shapley additive explanations analysis revealed a hierarchical learning mechanism in which the protocol-consistent energetic descriptor serves as the dominant thermodynamic anchor, whereas structural descriptors provide secondary refinements that improve adsorption-energy discrimination, particularly near the thermoneutral regime relevant to catalyst screening. Explicit evaluation of MLIP-only geometry workflows further demonstrated that the framework retains meaningful adsorption-energy ranking capability despite degradation introduced by MLIP-relaxed geometries. Additional OOD and transfer-learning analyses showed that predictive robustness depends strongly on the balance between compositional diversity and reference-protocol consistency. These results establish energetically anchored MLIP embeddings as an effective strategy for scalable post-DFT adsorption-energy refinement and data-efficient electrocatalyst screening while clarifying the practical limitations of MLIP-driven workflows for heterogeneous catalysis.