InvDesMobility: a reliability-gated first-principles feedback framework for closed-loop materials discovery
2026-06-15 • Artificial Intelligence
Artificial Intelligence
AI summaryⓘ
The authors created InvDesMobility, a system that helps design new materials by predicting how well they conduct electricity. Their approach carefully checks the quality and reliability of the data from complex physics calculations before using it to improve predictions. Instead of just listing promising materials, they focused on making a trustworthy, repeatable process that learns from feedback to find better materials over time. This system can handle millions of possible materials and makes the design process more transparent and reliable.
inverse materials designcarrier mobilitydensity functional theory (DFT)first-principles calculationsfeedback frameworkgenerative modelacquisition rankingevidence stratification2D materialsclosed-loop discovery
Authors
Wen-Kao Li, Ze-Feng Gao, Peng-Jie Guo, Wei Ji, Zhong-Yi Lu
Abstract
Inverse materials design starts from target functionality and searches for structures that can realize it. Its value in closed-loop discovery depends not only on prediction performance, but also on whether expensive first-principles results are independently validated, provenance-recorded, and admitted as feedback only when evidence is sufficient. This is especially important for composite properties such as carrier mobility, where a final scalar value hides intermediate quantities, fit quality, convergence history, and workflow assumptions. Here we present InvDesMobility, a reliability-gated first-principles feedback framework that integrates multi-agent automated DFT, evidence stratification, generative structure proposal, acquisition ranking, and auditable release. Using 516 2DMatPedia-derived candidates, the workflow produced 280 QC-passed materials and 573 retained carrier-direction seed channels after channel-level reliability gating. These records were split into two feedback objects: relaxed structures updated the generative model, while retained mobility channels trained the acquisition model and set validation priority. Over multiple iterations, InvDesMobility screened 2.4 x 10^6 structures, submitted 102 candidates for DFT validation, and retained 86 reliability-gated generated channels across 41 formulas. Overall, the main contribution is not a fixed list of high-mobility materials, but a transferable feedback contract that makes closed-loop inverse design both useful and auditable when learning from expensive calculated properties. All source data, retained feedback records, and workflows are available at https://github.com/DreamLufei/invDesMobility, with an accompanying evidence website at https://dreamlufei.github.io/invDesMobility/.