Theoretical predictions of macroscopic performance (power conversion efficiencies [PCEs]) and experimental analyses for microscopic material (conformation) have always urged for organic photovoltaics. A series of acceptors based on multi‐conformation bistricyclic aromatic enes core have been designed. The results suggested that A4‐2, A5‐2, and T4‐2 show the full folded conformation, fitting, and exhibiting advantageous properties of various parts for acceptors effectively, thus getting high VOC and JSC (kCS/kCR exceeds 1012) as well. Their PCEs of devices matching different donors were predicted through machine learning (ML). In traditional device structures and crude environments, a maximum PCE is about seven times higher than original. Herein, a comprehensive investigation, ranging for conformations → donor/acceptor interfaces → morphology → PCEs, is carried out by pure theoretical methods. Therefore, this quantitative micro‐analysis combined with the ML intelligent prediction leads to a new approach in the development of the next generation of nonfullerene acceptors.