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 MF62– 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 – t2g) and F­(2p) states that distinguishes the TiF62– anion from the ZrF62– and HfF62– anions.