Cu-Ni Oxidation Mechanism Unveiled: A Machine Learning-Accelerated First-Principles and TEM Study.

The development of accurate methods for determining how alloy surfaces spontaneously restructure under reactive and corrosive environments is a key, long-standing, grand challenge in materials science. Using machine learning-accelerated density functional theory and rare-event methods, in conjunction with environmental transmission electron microscopy (ETEM), we examine the interplay between surface reconstructions and preferential segregation tendencies of CuNi(100) surfaces under oxidation conditions. Our modeling approach predicts that oxygen-induced Ni segregation in CuNi alloys favors Cu(100)-O c(2 × 2) reconstruction and destabilizes the Cu(100)-O (2√2 × √2)45° missing row reconstruction (MRR).

Overcoming Inaccuracies in Machine Learning Interatomic Potential Implementation for Ionic Vacancy Simulations.

Machine learning interatomic potentials, particularly ones based on deep neural networks, have taken significant strides in accelerating first-principles simulations, expanding the length and time scales of the simulations with accuracies akin to first-principles simulations. Notwithstanding their success in accurately describing the physical properties of pristine ionic systems with multiple oxidation states, herein we show that an implementation of deep neural network potentials (DNPs) yield vacancy formation energies in MgO with a significant ∼3 eV error. In contrast, we show that moment tensor potentials can accurately describe all properties of the oxide, including vacancy formation energies.

Cryptate binding energies towards high throughput chelator design: metadynamics ensembles with cluster-continuum solvation.

A tiered forcefield/semiempirical/-GGA pipeline together with a thermodynamic scheme designed with error cancellation in mind was developed to calculate binding energies of [2.2.2] cryptate complexes of mono- and divalent cations.

Theoretical Prediction of Thermal Expansion Anisotropy for YSiO Environmental Barrier Coatings Using a Deep Neural Network Potential and Comparison to Experiment.

Environmental barrier coatings (EBCs) are an enabling technology for silicon carbide (SiC)-based ceramic matrix composites (CMCs) in extreme environments such as gas turbine engines. However, the development of new coating systems is hindered by the large design space and difficulty in predicting the properties for these materials. Density Functional Theory (DFT) has successfully been used to model and predict some thermodynamic and thermo-mechanical properties of high-temperature ceramics for EBCs, although these calculations are challenging due to their high computational costs.

Exploring the formation of gold/silver nanoalloys with gas-phase synthesis and machine-learning assisted simulations.

While nanoalloys are of paramount scientific and practical interest, the main processes leading to their formation are still poorly understood. Key structural features in the alloy systems, including the crystal phase, chemical ordering, and morphology, are challenging to control at the nanoscale, making it difficult to extend their use to industrial applications. In this contribution, we focus on the gold/silver system that has two of the most prevalent noble metals and combine experiments with simulations to uncover the formation mechanisms at the atomic level.