Efficient Amino Acid Conformer Search with Bayesian Optimization

Finding low-energy molecular conformers
is challenging due to the
high dimensionality of the search space and the computational cost
of accurate quantum chemical methods for determining conformer structures
and energies. Here, we combine active-learning Bayesian optimization
(BO) algorithms with quantum chemistry methods to address this challenge.
Using cysteine as an example, we show that our procedure is both efficient
and accurate. After only 1000 single-point calculations and approximately
80 structure relaxations, which is less than 10% computational cost
of the current fastest method, we have found the low-energy conformers
in good agreement with experimental measurements and reference calculations.
To test the transferability of our method, we also repeated the conformer
search of serine, tryptophan, and aspartic acid. The results agree
well with previous conformer search studies.