Reliability-Aware Constitutive Learning for Materials Discovery

Accurate characterization of material behavior at elevated temperatures is essential for engineering design but remains costly and time-consuming due to the extensive experimental testing required across multiple thermo-mechanical conditions. Recent advances in high-throughput mechanical testing workflows coupled with machine learning have enabled rapid prediction of stress-strain behavior from limited experimental measurements. However, existing approaches often lack interpretability and reliability assessment, provide limited support for out-of-distribution generalization to unseen alloy families, and offer no guarantees of physically meaningful stress-strain trajectories. We present a physics-aware constitutive learning framework that combines trajectory-aware training, a shape-aware loss, and a per-temperature mean stress loss to improve trajectory fidelity while preserving temperature-dependent material response. Beyond prediction accuracy, we introduce a reliability framework that quantifies neighborhood support within a low-dimensional constitutive materials space, providing an interpretable measure of extrapolation risk for previously unseen materials. We further propose a coverage-disagreement framework that identifies under-sampled thermo-mechanical regions and provides quantitative guidance for targeted experimental data acquisition. Across leave-one-material-out evaluations and previously unseen alloy families, the proposed framework produces physically consistent stress-strain curves while achieving average prediction errors below 5% on previously unseen materials. Together, these contributions transform constitutive machine learning from a prediction-focused task into a reliability-aware decision-support framework capable of assessing extrapolation risk and guiding future experimentation.