Solution-based processing of photovoltaic materials has important advantages, including lower overall costs and easier upscaling of fabrication, where the solvents play an essential role. Antisolvent crystallization technique is often applied to achieve high-quality perovskite materials. However, the basic chemistry of the solvent-antisolvent crystallization is still not well understood. Besides, common antisolvents are toxic, and their number is limited. In this work, a novel robot-based method was used to screen the efficient antisolvents for different solvent-perovskite systems. 336 combinations of perovskite-solvent-antisolvent could be prepared and characterized by the integrated robotic platform in 2 days. In addition, we provided a detailed working mechanism of the solvent-antisolvent crystallization approach. Furthermore, hundreds of potential antisolvents were proposed based on high-throughput screening and simulation. Verification tests match well with theory, and all reported antisolvents used for photovoltaic device optimizations are within our predicted range of the Hansen space, indicating that the predictions are reliable.

基于溶液的光伏材料加工具有重要优势，包括更低的总体成本和更容易的制造规模提升，其中溶剂起着至关重要的作用。抗溶剂结晶技术通常用于获得高质量的钙钛矿材料。然而，对溶剂-反溶剂结晶的基本化学方法仍知之甚少。此外，普通的抗溶剂是有毒的，并且其数量是有限的。在这项工作中，一种新颖的基于机器人的方法被用于筛选适用于不同溶剂钙钛矿体系的高效抗溶剂。可以在2天内通过集成的机器人平台制备336种钙钛矿-溶剂-抗溶剂组合，并对其进行表征。另外，我们提供了溶剂-反溶剂结晶方法的详细工作机制。此外，基于高通量筛选和模拟，提出了数百种潜在的抗溶剂。验证测试与理论非常吻合，用于光伏设备优化的所有报道的反溶剂均在我们对汉森空间的预测范围内，表明该预测是可靠的。