Machine-Learning-Assisted Synthesis of Polar Racemates

Racemates have recently received attention as nonlinear optical and piezoelectric materials. Here, a machine-learning-assisted composition space approach was applied to synthesize the missing M = Ti, Zr members of the Δ,Λ-[Cu(bpy)₂(H₂O)]₂[MF₆]₂·3H₂O (M = Ti, Zr, Hf; bpy = 2,2'-bipyridine) family (space group: Pna2₁). In each (CuO, MO₂)/bpy/HF(aq) (M = Ti, Zr, Hf) system, the polar noncentrosymmetric racemate (M-NCS) forms in competition with a centrosymmetric one-dimensional chain compound (M-CS) based on alternating Cu(bpy)(H₂O)₂²⁺ and MF₆^2- basic building units (space groups: Ti-CS (Pnma), Zr-CS (P1̅), Hf-CS (P2/n)). Machine learning models were trained on reaction parameters to gain unbiased insight into the underlying statistical trends in each composition space. A human-interpretable decision tree shows that phase selection is driven primarily by the bpy:CuO molar ratio for reactions containing Zr or Hf, and predicts that formation of the Ti-NCS compound requires that the amount of HF present be decreased to raise the pH, which we verified experimentally. Predictive leave-one-metal-out (LOO) models further confirm that behavior in the Ti system is distinct from that of the Zr and Hf systems. The chemical origin of this distinction was probed via fluorine K-edge X-ray absorption spectroscopy. Pre-edge features in the F1s X-ray absorption spectra reveal the strong ligand-to-metal π bonding between Ti(3d - t) and F(2p) states that distinguishes the TiF₆^2- anion from the ZrF₆^2- and HfF₆^2- anions.