  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
one thousand single-point calculations and approximately thirty 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.
