A review of artificial intelligence in materials screening Dat 2026 Next Ma

Rapid and reliable screening of material evidence is central to contemporary forensic science, yet conventional analytical workflows remain constrained by limited throughput, subjective interpretation, and the inherent complexity of heterogeneous, trace-level samples. In parallel, artificial intelligence (AI) has catalyzed a paradigm shift in high-throughput materials screening across domains such as energy storage, catalysis, and polymer science by leveraging large-scale computational and experimental datasets, engineered descriptors, and advanced machine learning (ML) and deep learning (DL) architectures. This review bridges these two trajectories by systematically examining how AI-based materials screening frameworks can be adapted and extended for forensic applications. Drawing on the conceptual infrastructure of mainstream AI-driven materials discovery, we provide a comprehensive analysis of (i) the nature and analytical challenges of forensic material evidence; (ii) data acquisition, representation, and pre-processing strategies tailored to forensic laboratories; (iii) AI models for classification, identification, similarity assessment, and inverse design; and (iv) real-world and emerging applications in the analysis of fibers, paints, glass, soils, polymers, metals, and chemical residues. Case studies illustrate how AI-enhanced Fourier-transform infrared spectroscopy (FTIR), Raman and surface-enhanced Raman spectroscopy (SERS), gas chromatography – mass spectrometry (GC-MS/LC-MS), and hyperspectral imaging have measurably improved discrimination power, throughput, and objectivity in operational forensic laboratories. Integration with forensic quality frameworks, including ISO/IEC 17025 and Daubert admissibility criteria, is addressed with emphasis on explainability, rigorous validation, and data governance. Looking ahead, the review outlines opportunities for autonomous forensic error rate studies.