Differentiable Optimization for the Prediction of Ground State Structures (DOGSS).

Ground state or relaxed inorganic structures are the starting point for most computational materials science or surface science analyses. Many of these structure relaxations represent systematic changes to the structure, but there are currently no general methods to improve the initial structure guess based on past calculations. Here we present a method to directly predict the ground state configuration using differentiable optimization and graph neural networks to learn the properties of a simple harmonic force field that approximates the ground state structure and properties. We demonstrate this flexible open source tool for improving the initial configurations for large datasets of inorganic multicomponent surface relaxations across 32 elements and the relaxation of adsorbates (H and CO) on these surfaces. Using these improved initial configurations reduces the expensive adsorbate-covered surface relaxations by approximately 50% and is complementary to other approaches to accelerate the relaxation process.

Capturing Structural Transitions in Surfactant Adsorption Isotherms at Solid/Solution Interfaces.

Although adsorption isotherms of surfactants are critical in determining the relationship between interfacial properties and structures of surfactants, providing quantitative predictions of the isotherms remains challenging. This is especially true for adsorption at hard interfaces such as on two-dimensional (2D) layered materials or on nanoparticles where simulation techniques developed for fluid-fluid interfaces that dynamically change surface properties by adjusting unit cells do not apply. In this work, we predict nonideal adsorption at a solid-solution interface with a molecular thermodynamic theory (MTT) model that utilizes molecular dynamics (MD) simulations for the determination of free-energy parameters in the MTT. Furthermore, the MD/MTT model provides atomistic insights into the nonideal behavior of surfactants by capturing structural phases of the surfactants at the interface. Our approach captures structural transitions from the ideal state at low concentrations and then to the critical surface aggregation concentration (CSAC) and finally through the critical micelle concentration (CMC). We validate our model against the original MTT model by comparing predicted adsorption isotherms of a simplified surfactant system from both approaches. We further substantiate the applicability of our model in complex systems by providing adsorption isotherms in an aqueous sodium dodecyl sulfate (SDS)-graphene system, in good agreement with the experimental observations of the CSAC for the same system.

Convolutional Neural Network of Atomic Surface Structures To Predict Binding Energies for High-Throughput Screening of Catalysts.

High-throughput screening of catalysts can be performed using density functional theory calculations to predict catalytic properties, often correlated with adsorbate binding energies. However, more complete investigations would require an order of 2 more calculations compared to the current approach, making the computational cost a bottleneck. Recently developed machine-learning methods have been demonstrated to predict these properties from hand-crafted features but have struggled to scale to large composition spaces or complex active sites. Here, we present an application of a deep-learning convolutional neural network of atomic surface structures using atomic and Voronoi polyhedra-based neighbor information. The model effectively learns the most important surface features to predict binding energies. Our method predicts CO and H binding energies after training with 12 000 data for each adsorbate with a mean absolute error of 0.15 eV for a diverse chemical space. Our method is also capable of creating saliency maps that determine atomic contributions to binding energies.