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Density functional theory (DFT) is a powerful computational method used toobtain physical and chemical properties of materials. In the materialsdiscovery framework, it is often necessary to virtually screen a large andhigh-dimensional chemical space to find materials with desired properties.However, grid searching a large chemical space with DFT is inefficient due toits high computational cost. We propose an approach utilizing Bayesianoptimization (BO) with an artificial neural network kernel to enable smartsearch. This method leverages the BO algorithm, where the neural network,trained on a limited number of DFT results, determines the most promisingregions of the chemical space to explore in subsequent iterations. Thisapproach aims to discover materials with target properties while minimizing thenumber of DFT calculations required. To demonstrate the effectiveness of thismethod, we investigated 63 doped graphene quantum dots (GQDs) with sizesranging 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 significantreduction in computational cost, approximately 20% of what would be requiredfor a full grid search, by employing the BO algorithm with a neural networkkernel. Considering that TDDFT calculations for a single GQD require about halfa day of wall time on high-performance computing nodes, this reduction issubstantial. Our approach can be generalized to the discovery of new drugs,chemicals, crystals, and alloys with high-dimensional and large chemicalspaces, offering a scalable solution for various applications in materialsscience.

Rapid Discovery of Graphene Nanocrystals Using DFT and Bayesian Optimization with Neural Network Kernel