From Autonomous Knowledge Generation to Generalizable Property Prediction: A Precision-First Pipeline for Electro-Optic Materials Discovery

Abstract

Electro-optic materials, whose refractive index changes linearly with an applied electric field (the Pockels effect), set the speed and energy of optical modulators and microwave-to-optical links for quantum computing. Designing better materials requires optimizing composition alongside temperature, strain, and bias field. However, general repositories lack electro-optic tensor data. We present a pipeline that builds such a database from literature following the rule: "no value is better than a wrong value." The pipeline has five stages: physics-aware acquisition, layout-aware extraction via LLM and deterministic table parsers, a three-tier verification gate, cross-paper consensus using median and median absolute deviation, and a noise-robust surrogate model audited by leave-one-material-out (LOMO) testing. Physics enters directly: notation is mapped, point-group symmetry-forbidden Pockels components are rejected, equivalent labels are merged, and values carry provenance tags. In a run over 49 publications, the gate processed 816 candidates into 414 accepted, 215 held for review, and 187 rejected. Against a hand-certified gold standard of 303 values from 16 papers, the run achieved a precision of 0.51 and recall of 0.48 for exact record matches. Across four database snapshots, held-out materials where the surrogate model beat a predict-the-mean baseline grew from 1 of 4 to 9 of 13 as the database expanded from 10 to 22 materials. While not a controlled comparison, this trend supports the premise that a precision-oriented, physics-checked database can power forward models beyond its training set and clarifies requirements for inverse material design.