AI-Driven Multiscale Modelling for the In Silico Toxicity Profiling of Novel Alloys

The accelerated development of refractory high-entropy alloys (HEAs) offers unprecedented mechanical performance in extreme environments, such as aerospace engineering and advanced nuclear applications. However, the inherent compositional complexity of these multi-principal element alloys necessitates rigorous, concurrent assessments of their ecological and biological impacts. Traditional empirical testing for multi-element material degradation and synergistic ion leaching is both cost-prohibitive and time-intensive. To overcome these limitations, this study presents an integrated computational workflow combining artificial intelligence (AI) and multiscale modelling to proactively predict the environmental toxicity of novel HEA compositions. Advanced machine learning architectures, including ensemble algorithms and deep neural networks, were utilized to develop robust Quantitative Structure–Activity Relationship (QSAR) models. These models rapidly screen vast compositional spaces to predict the ecotoxicological profiles of various multi-metal and metal–ligand complexes generated during aqueous corrosion and environmental degradation. To mechanistically validate these AI-driven macro-scale predictions, fundamental atomic-level simulations were deployed. Density Functional Theory (DFT) and classical Molecular Dynamics (MD) were coupled with high-throughput molecular docking protocols to calculate the precise binding affinities, interaction mechanisms, and thermodynamic stabilities between leached metallic species and critical biological receptors. By bridging these microscopic biophysical interactions with macroscopic environmental transport profiles, this multiscale approach maps the comprehensive lifecycle of the material. The resulting predictive framework not only accelerates the discovery of high-performance alloys by circumventing rudimentary trial-and-error toxicity assays, but it also ensures that next-generation structural materials are engineered with foundational ecological safety parameters. Ultimately, this methodology signifies a critical paradigm shift toward sustainable computational metallurgy, seamlessly integrating in silico biophysical toxicity profiling directly into the initial stages of the materials design pipeline.