Efficiently designing lightweight alloys with combined high corrosion resistance and mechanical properties remains an enduring topic in materials engineering. Due to the inadequate accuracy of conventional stress-strain machine learning (ML) models caused by corrosion factors, a novel reinforcement self-learning ML algorithm combined with calculated features (accuracy R² >0.92) is developed. Based on the ML models, calculated work functions and mechanical moduli, a Computation Designed Corrosion-Resistant Al alloy is fabricated and verified. The performance (elongation reaches ∼30 %) is attributed to the H trapping Al-Sc-Cu phases (-1.44 eV H⁻¹) and Cu-modified η/η' precipitates inside the grain boundaries (GBs).

Corrosion jeopardizes the materials longevity and engineering safety, hence the corrosion rate needs to be forecasted so as to better guide materials selection. Although field exposure experiments are dependable, the prohibitive cost and their time-consuming nature make it difficult to obtain large dataset for machine learning. Here, we propose a strategy Integrating Ab-initio Calculations with Random Forest (IACRF) to optimize the model, thereby estimating the corrosion rate of Al alloys in diverse environments. Based on the thermodynamic assessment of the secondary phases, the ab-initio calculation quantities, especially the work function, significantly improved the prediction accuracy with respect to small-sample Al alloys corrosion dataset. To build a better generic prediction model, the most accessible and effective features are identified to train IACRF. Finally, the independent field exposure experiments in Southeast Asia have proven the generalization ability of IACRF in which the average prediction accuracy is improved up to 91%.