MDG-IS: An Iterative Sampling Framework For Theory-Experiment Data Alignment In Artificial Intelligence Chemistry

AI4Chemistry faces a fundamental bottlewneck: high-fidelity experimental data are scarce and heterogeneous, while large low-fidelity theoretical datasets are abundant but subdomain-biased. This mismatch leads to distribution misalignment and frequent negative transfer, limiting the effectiveness of existing transfer learning and multi-fidelity approaches that assume global theory–experiment consistency. We propose Multi-SubDomain GMM-guided Iterative Sampling (MDG-IS), a fidelity-aware data selection framework that identifies which theoretical samples are actually useful for learning from real experimental data. MDG-IS models experimental subdomains through a Gaussian Mixture Model and performs local KL-based alignment to iteratively extract a compact, well-aligned subset of theoretical data. Across molecular, reaction, and materials benchmarks, MDG-IS markedly reduces computational cost, mitigates negative transfer, and consistently improves prediction accuracy in real scenarios.