Reaction coordinates in complex systems-a perspective

In molecular simulations, the identification of suitable reaction coordinates is central to both the analysis and sampling of transitions between metastable states in complex systems. If sufficient simulation data are available, a number of methods have been developed to reduce the vast amount of high-dimensional data to a small number of essential degrees of freedom representing the reaction coordinate. Likewise, if the reaction coordinate is known, a variety of approaches have been proposed to enhance the sampling along the important degrees of freedom. Often, however, neither one nor the other is available. One of the key questions is therefore, how to construct reaction coordinates and evaluate their validity. Another challenges arises from the physical interpretation of reaction coordinates, which is often addressed by correlating physically meaningful parameters with conceptually well-defined but abstract reaction coordinates. Furthermore, machine learning based methods are becoming more and more applicable also to the reaction coordinate problem. This perspective highlights central aspects in the identification and evaluation of reaction coordinates and discusses recent ideas regarding automated computational frameworks to combine the optimization of reaction coordinates and enhanced sampling

Automated free-energy calculation from atomistic simulations

We devise automated workflows for the calculation of Helmholtz and Gibbs free energies and their temperature and pressure dependence and provide the corresponding computational tools. We employ nonequilibrium thermodynamics for evaluating the free energy of solid and liquid phases at a given temperature and reversible scaling for computing free energies over a wide range of temperatures, including the direct integration of P−T coexistence lines. By changing the chemistry and the interatomic potential, alchemical and upscaling free energy calculations are possible. Several examples illustrate the accuracy and efficiency of our implementation

Neural network based path collective variables for enhanced sampling of phase transformations

We propose a rigorous construction of a 1D path collective variable to sample structural phase transformations in condensed matter. The path collective variable is defined in a space spanned by global collective variables that serve as classifiers derived from local structural units. A reliable identification of local structural environments is achieved by employing a neural network based classification. The 1D path collective variable is subsequently used together with enhanced sampling techniques to explore the complex migration of a phase boundary during a solid-solid phase transformation in molybdenum

First-principles statistical mechanics study of the stability of a sub-nanometer thin surface oxide in reactive environments: CO oxidation at Pd(100)

We employ a multiscale modeling approach to study the surface structure and composition of a Pd(100) model catalyst in reactive environments. Under gas phase conditions representative of technological CO oxidation (~1 atm, 300-600 K) we find the system on the verge of either stabilizing sub-nanometer thin oxide structures or CO adlayers at the surface. Under steady-state operation this suggests the presence or continuous formation and reduction of oxidic patches at the surface, which could be key to understand the observable catalytic function.Comment: 4 pages including 2 figures; related publications can be found at http://www.fhi-berlin.mpg.de/th/th.htm

Trends in elastic properties of Ti–Ta alloys from first-principles calculations

The martensitic start temperature (Ms) is a technologically fundamental characteristic of high-temperature shape memory alloys. We have recently shown [Chakraborty et al 2016 Phys. Rev. B 94 224104] that the two key features in describing the composition dependence of Ms are the T = 0 K phase stability and the difference in vibrational entropy which, within the Debye model, is directly linked to the elastic properties. Here, we use density functional theory together with special quasi-random structures to study the elastic properties of disordered martensite and austenite Ti–Ta alloys as a function of composition. We observe a softening in the tetragonal shear elastic constant of the austenite phase at low Ta content and a non-linear behavior in the shear elastic constant of the martensite. A minimum of 12.5% Ta is required to stabilize the austenite phase at T = 0 K. Further, the shear elastic constants and Young's modulus of martensite exhibit a maximum for Ta concentrations close to 30%. Phenomenological, elastic-constant-based criteria suggest that the addition of Ta enhances the strength, but reduces the ductile character of the alloys. In addition, the directional elastic stiffness, calculated for both martensite and austenite, becomes more isotropic with increasing Ta content. The reported trends in elastic properties as a function of composition may serve as a guide in the design of alloys with optimized properties in this interesting class of materials

First principles characterization of reversible martensitic transformations

Reversible martensitic transformations (MTs) are the origin of many fascinating phenomena, including the famous shape memory effect. In this work, we present a fully ab initio procedure to characterize MTs in alloys and to assess their reversibility. Specifically, we employ ab initio molecular dynamics data to parametrize a Landau expansion for the free energy of the MT. This analytical expansion makes it possible to determine the stability of the high- and low-temperature phases, to obtain the Ehrenfest order of the MT, and to quantify its free energy barrier and latent heat. We apply our model to the high-temperature shape memory alloy Ti-Ta, for which we observe remarkably small values for the metastability region (the interval of temperatures in which the high-and low-temperature phases are metastable) and for the barrier: these small values are necessary conditions for the reversibility of MTs and distinguish shape memory alloys from other materials