Systematic coarse graining of the electronic structure for atomistic modelling of high-temperature materials

Density Functional Theory (DFT) enables the accurate prediction of many properties of high-temperature materials, but it is often difficult to reach experimental length and time scales or to represent the chemical complexity of a Ni-based superalloy directly from DFT. In these cases simplified representations of the interatomic interaction are required that enable simulations on relevant length and time scales. In this talk I will discuss the derivation and application of simplified models of the interatomic interaction. Examples will be given for the diffusion of refractory elements in Ni- and Co-based alloys, the modelling of phase stability in TCP phases, mechanical properties of high-entropy alloys, transformation temperatures in shape memory alloys and the atomistic simulation of phase transformation kinetics

Analytic bond-order potentials: from the electronic structure to million atom simulations

The analytic bond-order potentials (BOPs) are derived by systematically coarse graining the electronic structure from density functional theory into effective interatomic interactions. This derivation comprises expressions for charge transfer and magnetism, including noncollinear magnetism, that are obtained directly from density functional theory, so that the BOPs constitute a coherent simplified description for modeling bond making and breaking in covalent and metallic materials. Similar to the structure and functional form of the BOPs, their parameterization may also be obtained by projection from density functional theory. In this discussion, I will outline the structure and derivation of the BOPs and summarize their parameterization. I will discuss applications of the BOPs in simulations of refractory elements, iron and iron carbon. The relation of the BOPs to other methods such as recursion, the Kernel Polynomial Method, or the Fermi Operator Expansion will be briefly reviewed and the integration of the BOPs into established codes for simulations with millions of atoms will be highlighted

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

Graph Atomic Cluster Expansion for semilocal interactions beyond equivariant message passing

The Atomic Cluster Expansion provides local, complete basis functions that enable efficient parametrization of many-atom interactions. We extend the Atomic Cluster Expansion to incorporate graph basis functions. This naturally leads to representations that enable the efficient description of semilocal interactions in physically and chemically transparent form. Simplification of the graph expansion by tensor decomposition results in an iterative procedure that comprises current message-passing machine learning interatomic potentials. We demonstrate the accuracy and efficiency of the graph Atomic Cluster Expansion for a number of small molecules, clusters and a general-purpose model for carbon. We further show that the graph Atomic Cluster Expansion scales linearly with number of neighbors and layer depth of the graph basis functions