Steen Lysgaard

TL;DR: The atomic simulation environment (ASE) provides modules for performing many standard simulation tasks such as structure optimization, molecular dynamics, handling of constraints and performing nudged elastic band calculations.

TL;DR: The machine learning accelerated approach yields a 50-fold reduction in the number of required energy calculations compared to a traditional “brute force” genetic algorithm, which makes searching through the space of all homotops and compositions of a binary alloy particle in a given structure feasible.

TL;DR: An adsorbate-dependent correction scheme is presented, which enables an accurate determination of adsorption energies using a computationally fast, localized LCAO-basis set and shows that it is possible to use the L CAO mode to obtain a realistic estimate of the molecular chemisorption energy for systems where the computation in normal grid mode is not computationally feasible.

TL;DR: In this article, the same authors applied equilibrium pressure measurements from ammonia desorption, X-ray powder diffraction and density functional theory calculations to identify thermodynamically stable Strontium chloride octamine, Sr(NH3)Cl2, Sr (NH 3)2Cl2 and Sr( NH 3)8Cl2 phases, and solved the crystal structures in the space groups Cmcm, Aem2 and Pnma.

TL;DR: A method for predicting the optimal composition and structure of alloy nanoparticles and clusters, with particular focus on the surface properties, is presented, based on a genetic algorithm that work by interchanging positions of elements depending on their local environment and position in the cluster.