Accelerating the experimental cycle for new materials development is vital for addressing the grand energy challenges of the 21^st century. We fabricate and characterize 75 unique perovskite-inspired compositions within a 2-month period, with 87% exhibiting band gaps between 1.2 and 2.4 eV, which are of interest for energy-harvesting applications. We utilize a fully connected deep neural network to classify compounds based on experimental X-ray diffraction data into 0D, 2D, and 3D structures, more than 10 times faster than human analysis and with 90% accuracy. We validate our methods using lead-halide perovskites and extend the application to lead-free compositions. The wider synthesis window and faster cycle of learning enables the realization of a multi-site lead-free alloy series, Cs₃(Bi_1-xSb_x)₂(I_1-xBr_x)₉. We reveal the non-linear band-gap behavior and transition in dimensionality upon simultaneous alloying on the B-site and X-site of Cs₃Bi₂I₉ with Sb and Br.

加快新材料的开发，实验周期是用于寻址21宏伟的能源挑战至关重要^ST世纪。我们在2个月内制作并表征了75种独特的钙钛矿启发型组合物，其中87％的带隙介于1.2和2.4 eV之间，这对于能量收集应用很感兴趣。我们利用完全连接的深度神经网络将基于实验X射线衍射数据的化合物分类为0D，2D和3D结构，比人类分析快10倍以上，准确率达90％。我们验证了使用卤化钙钛矿的方法，并将其应用范围扩展到了无铅成分。更大的合成窗口和更快的学习周期，使多位无铅合金系列Cs _{3的实现成为可能}（Bi _1-x Sb _x）₂（I _1-x Br _x）₉。我们揭示了在带Sb和Br的Cs ₃ Bi ₂ I ₉的B位和X位同时合金化时的非线性带隙行为和尺寸跃迁。