  Density functional theory (DFT) is a powerful computational method used to
obtain physical and chemical properties of materials. In the materials
discovery framework, it is often necessary to virtually screen a large and
high-dimensional chemical space to find materials with desired properties.
However, grid searching a large chemical space with DFT is inefficient due to
its high computational cost. We propose an approach utilizing Bayesian
optimization (BO) with an artificial neural network kernel to enable smart
search. This method leverages the BO algorithm, where the neural network,
trained on a limited number of DFT results, determines the most promising
regions of the chemical space to explore in subsequent iterations. This
approach aims to discover materials with target properties while minimizing the
number of DFT calculations required. To demonstrate the effectiveness of this
method, we investigated 63 doped graphene quantum dots (GQDs) with sizes
ranging from 1 to 2 nm to find the structure with the highest light absorbance.
Using time-dependent DFT (TDDFT) only 12 times, we achieved a significant
reduction in computational cost, approximately 20% of what would be required
for a full grid search, by employing the BO algorithm with a neural network
kernel. Considering that TDDFT calculations for a single GQD require about half
a day of wall time on high-performance computing nodes, this reduction is
substantial. Our approach can be generalized to the discovery of new drugs,
chemicals, crystals, and alloys with high-dimensional and large chemical
spaces, offering a scalable solution for various applications in materials
science.
