[This abstract is based on the authors' abstract.] Experiments related to nanofabrication often face challenges of resource-limited experimental budgets, highly demanding tolerance requirements, and complicated response surfaces. Therefore, wisely selecting design points is crucial in order to minimize the expense of resources while at the same time ensuring that enough information is gained to accurately address the experimental goals. In this paper, an eﬃcient batch-sequential design methodology is proposed for optimizing high-cost, low-resource experiments with complicated response surfaces. Through the sequential learning of the unknown response surface, the proposed method sequentially narrows down the design space to more important subregions and selects a batch of design points in the reduced design region. The proposed method balances the space ﬁlling of the design region and the search for the optimal operating condition. The performance of the proposed method is demonstrated on a nanowire synthesis system as well as on an optimization test function.