Causal Intervention Active Learning for Oxide Material Screening: Balancing Structure Identification and Prediction

Material screening, including oxide catalyst discovery, remains constrained by costly synthesis-and-test cycles, making sample-efficient experiment selection a central challenge. Active learning can accelerate this process, but standard acquisition rules mainly optimize predictive utility and offer limited support for clarifying the causal structure behind material-screening targets. CIAL is a causal-structure-aware acquisition framework. It combines an intervention-inspired graph-structure term with expected improvement, using an adaptive schedule that begins with structure clarification and shifts toward prediction-oriented exploitation. On synthetic benchmarks with known ground-truth causal graphs, CIAL reduces final-iteration structural Hamming distance by 76.2% relative to the standard EI baseline; significance is assessed using two-sided Welch’s t-tests on final-iteration metrics across five random seeds, with p=0.0036 for SHD and p<0.001 for F1. For real-data screening, CIAL is evaluated on a Materials Project transition metal oxide benchmark built from filtered oxide entries with formation-energy labels and generic bulk/compositional descriptors. Formation energy is an upstream stability-relevant property, not a direct catalytic activity label. On the main MP benchmark, CIAL is competitive with EI; its clearest advantage over EI appears under NOTEARS-based cross-split validation, but random sampling achieves the highest inferred-reference graph agreement in several real-data settings. An exploratory OC20 catalyst dataset check follows the same ordering but remains statistically inconclusive. Ablation results show that neither the causal nor the predictive component alone recovers the full benefit of the combined policy. CIAL improves graph recovery on controlled synthetic benchmarks; the real-data evidence is mixed and protocol-dependent, establishing CIAL as a proof-of-concept structure-aware acquisition method rather than a validated catalyst-discovery framework.