High-Throughput Exploration of Ti-V-Nb-Mo Carbide MXenes Using Neural Network Potentials and Their Evaluation as Catalysts for Hydrogen Evolution Reaction.

Realization of a sustainable hydrogen economy in the future requires the development of efficient and cost-effective catalysts for its production at scale. MXenes (MX) are a class of 2D materials with 'n' layers of carbon or nitrogen (X) interleaved by 'n+1' layers of transition metal (M) and have emerged as promising materials for various applications including catalysts for hydrogen evolution reaction (HER). Their properties are intimately related to both their composition and their atomic structure.

Systematic Identification of Atom-Centered Symmetry Functions for the Development of Neural Network Potentials.

Neural network potentials are emerging as promising classical force fields that can enable long-time and large-length scale simulations at close to accuracies. They learn the underlying potential energy surface by mapping the Cartesian coordinates of atoms to system energies using elemental neural networks. To ensure invariance with respect to system translation, rotation, and atom index permutations, in the Behler-Parrinnello type of neural network potential (BP-NNP), the Cartesian coordinates of atoms are transformed into "structural fingerprints" using atom-centered symmetry functions (ACSFs).

Effect of temperature on concentrated electrolytes for advanced lithium ion batteries.

Salt-concentrated electrolytes are emerging as promising electrolytes for advanced lithium ion batteries (LIBs) that can offer high energy density and improved cycle life. To further improve these electrolytes, it is essential to understand their inherent behavior at various operating conditions of LIBs. Molecular dynamics (MD) simulations are extensively used to study various properties of electrolytes and explain the associated molecular-level phenomena.

Efficient lattice Boltzmann algorithm for Brownian suspensions.

A lattice Boltzmann (LB)-based hybrid method is developed to simulate suspensions of Brownian particles. The method uses conventional LB discretization (without fluid- level fluctuations) for suspending fluid, and treats Brownian particles as point masses with a stochastic thermal noise. LB equations are used to compute the velocity perturbations induced by the particle motion.