ChecMatE: A workflow package to automatically generate machine learning potentials and phase diagrams for semiconductor alloys.

Semiconductor alloy materials are highly versatile due to their adjustable properties; however, exploring their structural space is a challenging task that affects the control of their properties. Traditional methods rely on ad hoc design based on the understanding of known chemistry and crystallography, which have limitations in computational efficiency and search space. In this work, we present ChecMatE (Chemical Material Explorer), a software package that automatically generates machine learning potentials (MLPs) and uses global search algorithms to screen semiconductor alloy materials. Taking advantage of MLPs, ChecMatE enables a more efficient and cost-effective exploration of the structural space of materials and predicts their energy and relative stability with ab initio accuracy. We demonstrate the efficacy of ChecMatE through a case study of the InxGa1-xN system, where it accelerates structural exploration at reduced costs. Our automatic framework offers a promising solution to the challenging task of exploring the structural space of semiconductor alloy materials.

Resolving the odd-even oscillation of water dissociation at rutile TiO2(110)-water interface by machine learning accelerated molecular dynamics.

Aqueous rutile TiO2(110) is the most widely studied water-oxide interface, and yet questions about water dissociation are still controversial. Theoretical studies have systematically investigated the influence of the slab thickness on water dissociation energy (Ediss) at 1 monolayer coverage using static density functional theory calculation and found that Ediss exhibits odd-even oscillation with respect to the TiO2 slab thickness. However, less studies have accounted for the full solvation of an aqueous phase using ab initio molecular dynamics due to high computational costs in which only three, four, and five trilayer models of rutile(110)-water interfaces have been simulated. Here, we report Machine Learning accelerated Molecular Dynamics (MLMD) simulations of defect-free rutile(110)-water interfaces, which allows for a systematic study of the slab thickness ranging from 3 to 17 trilayers with much lower costs while keeping ab initio accuracy. Our MLMD simulations show that the dissociation degree of surface water (α) oscillates with the slab thickness and converges to ∼2% as the TiO2 slab becomes thicker. Converting α into dissociation free energy (ΔAdiss) and comparing with dissociation total energy Ediss calculated with a single monolayer of water, we find that the full solvation of the interfaces suppresses surface water from dissociating. It is interesting to note that the machine learning potential trained from the dataset containing exclusively the five trilayer TiO2 model exhibits excellent transferability to other slab thicknesses and further captures the oscillating behavior of surface water dissociation. Detailed analyses indicate that the central plane in odd trilayer slabs modulates the interaction between double trilayers and, thus, the bonding strength between terminal Ti and water, which affects pKa of surface water and water dissociation degree.

Recent Progress toward Ab Initio Modeling of Electrocatalysis.

Electrode potential is the key factor for controlling electrocatalytic reactions at electrochemical interfaces, and moreover, it is also known that the pH and solutes (e.g., cations) of the solution have prominent effects on electrocatalysis. Understanding these effects requires microscopic information on the electrochemical interfaces, in which theoretical simulations can play an important role. This Perspective summarizes the recent progress in method development for modeling electrochemical interfaces, including different methods for describing the electrolytes at the interfaces and different schemes for charging up the electrode surfaces. In the final section, we provide an outlook for future development in modeling methods and their applications to electrocatalysis.