Rapid Discovery of Graphene Nanoflakes with Desired Absorption Spectra Using DFT and Bayesian Optimization with Neural Network Kernel.

Abstract

Grid searching a large and high-dimensional chemical space with density functional theory (DFT) to discover new materials with desired properties is prohibitive due to the high computational cost. We propose an approach utilizing Bayesian optimization (BO) with an artificial neural network kernel to enable an efficient and low-cost guided search on the chemical space, avoiding costly brute-force grid search. This method leverages the BO algorithm, where the kernel 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 new 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 absorption. 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. 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 in high-dimensional and large chemical spaces, offering a scalable solution enabled by the neural network kernel.