Accessing the usefulness of atomic adsorption configurations in predicting the adsorption properties of molecules with machine learning.

We present a systematic study into the effect of adding atomic adsorption configurations into the training and validation dataset for a neural network's predictions of the adsorption energies of small molecules on single metal and bimetallic, single crystal surfaces. Specifically, we examine the efficacy of models trained with and without H and X atomic adsorption configurations, where X is C, N, or O, to predict XHn adsorption energies. In addition, we compare our machine learning models to traditional simple scaling relationships. We find that models trained with the atomic adsorption configurations outperform models trained with only molecular adsorption configurations, with as much as a 0.37 eV decrease in the MAE. We find that models trained with the atomic adsorption configurations slightly outperform traditional scaling relationships. In general, these results suggest it may be possible to vastly reduce the number of adsorption configurations one needs for training and validation datasets by supplementing said data with the adsorption configurations of composite atoms or smaller molecular fragments.

Combining Deep Learning Neural Networks with Genetic Algorithms to Map Nanocluster Configuration Spaces with Quantum Accuracy at Low Computational Cost.

The configuration spaces for bimetallic AuPd nanoclusters of various sizes are explored efficiently and analyzed accurately by combining genetic algorithms with neural networks trained on density functional theory. The methodology demonstrated herein provides an optimizable solution to the problem of searching vast configuration spaces with quantum accuracy in a way that is computationally practical. We implement a machine learning algorithm which learns the density functional theory potential with increasing performance while simultaneously generating and relaxing structures within the system's global configuration space unbiasedly. As a result, the algorithm naturally converges onto the system's energy minima while mapping the configuration space as a function of energy. The algorithm's simple design applies not only to nanocluster configurations, as demonstrated, but to bulk, substrate, and adsorption sites as well, and it is designed to scale. To demonstrate its computational efficiency, we work with AuPd nanoclusters of sizes 15, 20, and 25 atoms. Results focus primarily on evaluating the algorithm's performance; however, several physical insights into possible configurations for these nanoclusters naturally emerge as well, such as geometric Au surface segregation and stoichiometric Au minimization as a function of stability.

Competing adsorption mechanisms of pyridine on Cu, Ag, Au, and Pt(110) surfaces.

We explore the adsorption of pyridine on Cu, Ag, Au, and Pt(110) surfaces using density functional theory. To account for the van der Waals interaction, we use the optB86b-vdW, optB88-vdW, optPBE-vdW, revPBE-vdW, and rPW86-vdW2 functionals. For comparison, we also run calculations using the generalized gradient approximation-PBE (Perdew-Burke-Ernzerhof) functional. We find the most stable adsorption site to depend on both metal and functional, with two energetically favorable adsorption sites, namely, a vertically oriented site and a flat pyridine site. We calculate that every functional predicts pyridine to lie in the vertical configuration on the coinage metals at a low coverage. On Pt(110), by contrast, we calculate all the functionals-except rPW86-vdW2-to predict pyridine to lie flat at a low coverage. By analyzing these differences for these adsorption configurations, along with various geometric and electronic properties of the adsorbate/substrate system, we access in detail the performance of the 6 functionals we use. We also characterize the nature of the bonding of pyridine on the coinage metals from weak to strong physisorption, depending on the functional used. On Pt(110), we characterize the nature of the bonding of pyridine as ranging from strong physisorption to strong chemisorption depending again on the functional used, illustrating both the importance of the van der Waals interaction to this system and that this system can make a stringent test for computational methods.