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 V OC and J SC ( k CS / k CR exceeds 10 12 ) 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.