Computer-Assisted Synthesis Optimisation of Inorganic-Organic Hybrid Compounds Using the Local Optimisation Algorithm BOBYQA

Abstract

The local, deterministic optimisation algorithm BOBYQA (bound optimisation by quadratic approximation) was applied for the computer-assisted optimisation of inorganic–organic hybrid compounds. Four proof-of-concept studies were performed: 1) increasing the crystallinity and 2) inhibiting the crystallisation of [Ca(H2O)2(H2PMBC)2] (H2PMBC−=−HO3P-CH2-C4H6-COOH), 3) increasing the crystal size of [Bi(H2O)(BTC)] (BTC3−=benzene-1,3,5-tricarboxylate) and 4) tuning the particle sizes of [Al(OH)(CDC)]⋅x H2O (CDC2−=trans-1,4-cyclohexane dicarboxylate) to a desired value. The measurable quantities of crystallinity, crystal size and hydrodynamic diameter were used as the quality criteria for the optimisation, and the parameters of reaction time and temperature, molar ratios and overall concentration of the starting materials as well as the stirring rate were varied. The crystallinity of [Ca(H2O)2(H2PMBC)2] was increased in three optimisation steps by approximately 14 %, which was accompanied by an increase in crystal size by a factor of approximately 40. These crystals were suitable for structure determination from single-crystal X-ray diffraction data. The crystallisation of the same compound could be completely inhibited and clear solutions were obtained. The average crystal size of [Bi(H2O)(BTC)] was increased from (22.2±4.5) to (34.8±9.5) μm and the upper limit increased from 30.0 to 57.7 μm over the course of the optimisation. For the application in Bragg stacks, the variation of particle sizes of [Al(OH)(CDC)]⋅x H2O was studied. Although we aimed at a decrease to 100 nm, a lower limit of 460 nm and polydispersity index of 0.03 were accomplished. The convergence of the algorithm indicates that the optimisation progress is close to completion and the found value for the particle size is close to the lower limit in the system of the chosen parameters. These proofs-of-concept studies demonstrate the potential of optimisation algorithms like BOBYQA in synthesis optimisation experiments. At the same time, the convergence behaviour of the algorithm gives an indication of the progress of an optimisation.