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/.

翻译：逆向材料设计从目标功能出发，寻找能够实现该功能的结构。其在闭环发现中的价值不仅取决于预测性能，还取决于昂贵的第一性原理结果是否经过独立验证、来源记录完备，以及仅在证据充分时才被接纳为反馈。这对于载流子迁移率等复合性质尤为重要——最终标量值会隐藏中间量、拟合质量、收敛历史和工作流假设。本文提出InvDesMobility，一种可靠性门控的第一性原理反馈框架，集成了多智能体自动化DFT、证据分层、生成式结构提议、采集排序和可审计发布。基于516个2DMatPedia衍生候选物，该工作流在通道级可靠性门控后，产出280个QC通过的材料和573个保留的载流子方向种子通道。这些记录被拆分为两个反馈对象：弛豫结构用于更新生成模型，保留的迁移率通道用于训练采集模型并设定验证优先级。经过多轮迭代，InvDesMobility筛选了2.4 × 10^6个结构，提交102个候选物进行DFT验证，并保留了涵盖41种化学式的86个可靠性门控生成通道。总体而言，其主要贡献并非固定高迁移率材料清单，而是一种可迁移的反馈契约，使基于昂贵计算性质学习的闭环逆向设计兼具实用性与可审计性。全部源数据、保留的反馈记录及工作流均可在https://github.com/DreamLufei/invDesMobility获取，配套证据网站见https://dreamlufei.github.io/invDesMobility/。