Bayesian optimization for the traversal of molecular properties

In this work, we apply a Bayesian Optimization (BOpt) process to two sets of DFT-generated data of molecules containing transition metals to determine the resource savings that the process would generate when optimizing certain molecular properties.  We minimize the HOMO-LUMO energy gap in a database of 641 Manganese based coordination complexes with elements consisting of various combinations of planar-arranged ligands and the ferromagnetic-antiferromagnetic (FM-AFM) ground state energy difference in a database of 1081 Cobalt dimer based Single-Molecule magnets (SMM) with elements consisting of a static core region and different combinations of capping ligands.  We compare the behavior of several acuisition functions: probability of improvement (PI), a modified PI, and a custom acquisition function that estimates outcome probability via posterior smoothness.  We also employ a non-constant mean function which is based on a Bayesian posterior generated by a partial data set and a Fourier-Transform based descriptor for descriptors with cyclical symmetries.   We find that for these discrete data sets the greedy PI acquisition functions reliably enable sampling of molecules with low HOMO-LUMO gap and FM-AFM energies with much less resource expenditure than Monte-Carlo or brute force methods.