System and method for tuning compositions of high-entropy electrocatalysts using active generative graph learning

A system for optimizing high-entropy electrocatalyst compositions through active learning combined with deep generative graph models, comprising:

• a density functional theory (DFT) dataset generation module configured to generate and update a DFT database of atomic structures via spin-polarized DFT calculations;
• an atomic graph attention network (AGAT) training module configured to receive a DFT dataset from the DFT database and train at least one AGAT model that integrates translation, rotation, and permutation invariance;
• a high-throughput prediction module configured to apply the trained AGAT model to predict hydrogen evolution reaction (HER) activity for newly appended compositions selected from a dynamically updated formula list, thereby generating predicted compositions and their associated hydrogen adsorption free energy values;
• a conditional generative adversarial network (CGAN) generation module configured to receive the predicted compositions and the associated hydrogen adsorption free energy values from the high-throughput prediction module, to train multiple CGAN models conditioned on the predicted hydrogen adsorption free energy values, and to generate a plurality of hypothetical compositions using selected CGAN generator models;
• a validation and augmentation module configured to perform high-throughput DFT simulations on a selected subset of the generated hypothetical compositions to produce updated labeled datasets;
• a composition classification module configured to categorize the hypothetical compositions resulting from DFT validation performed by the validation and augmentation module using a k-nearest neighbors (KNN) model, and to append classification results to the formula list for use in subsequent active learning iterations;
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• a coordination module configured to orchestrate interactions among DFT dataset generation module, AGAT training module, high-throughput prediction module, CGAN generation module, validation and augmentation module, and composition classification module and to control the number of the active learning iterations.