Constrained dynamics of localized excitations causes a non-equilibrium phase transition in an atomistic model of glass formers

Dynamic facilitation theory assumes short-ranged dynamic constraints to be the essential feature of supercooled liquids and draws much of its conclusions from the study of kinetically constrained models. While deceptively simple, these models predict the existence of trajectories that maintain a high overlap with their initial state over many structural relaxation times. We use molecular dynamics simulations combined with importance sampling in trajectory space to test this prediction through counting long-lived particle displacements. For observation times longer than the structural relaxation time exponential tails emerge in the probability distribution of this number. Reweighting trajectories towards low mobility corresponds to a phase transition into an inactive phase. While dynamics in these two phases is drastically different structural measures show only slight differences. We discuss the choice of dynamic order parameter and give a possible explanation for the microscopic origin of the effective dynamic constraints.Comment: revised versio

Trial-based Heuristic Tree Search for MDPs with Factored Action Spaces

MDPs with factored action spaces, i.e. where actions are described as assignments to a set of action variables, allow reasoning over action variables instead of action states, yet most algorithms only consider a grounded action representation. This includes algorithms that are instantiations of the trial-based heuristic tree search (THTS) framework, such as AO* or UCT. To be able to reason over factored action spaces, we propose a generalisation of THTS where nodes that branch over all applicable actions are replaced with subtrees that consist of nodes that represent the decision for a single action variable. We show that many THTS algorithms retain their theoretical properties under the generalised framework, and show how to approximate any state-action heuristic to a heuristic for partial action assignments. This allows to guide a UCT variant that is able to create exponentially fewer nodes than the same algorithm that considers ground actions. An empirical evaluation on the benchmark set of the probabilistic track of the latest International Planning Competition validates the benefits of the approach

First-principles study of epitaxial strain in perovskites

Using an extension of a first-principles method developed by King-Smith and Vanderbilt [Phys. Rev. B {\bf 49}, 5828 (1994)], we investigate the effects of in-plane epitaxial strain on the ground-state structure and polarization of eight perovskite oxides: BaTiO$_3$, SrTiO$_3$, CaTiO$_3$, KNbO$_3$, NaNbO$_3$, PbTiO$_3$, PbZrO$_3$, and BaZrO$_3$. In addition, we investigate the effects of a nonzero normal stress. The results are shown to be useful in predicting the structure and polarization of perovskite oxide thin films and superlattices.Comment: 10 page

Symbolic Planning with Axioms

Axioms are an extension for classical planning models that allow for modeling complex preconditions and goals exponentially more compactly. Although axioms were introduced in planning more than a decade ago, modern planning techniques rarely support axioms, especially in cost-optimal planning. Symbolic search is a popular and competitive optimal planning technique based on the manipulation of sets of states. In this work, we extend symbolic search algorithms to support axioms natively. We analyze different ways of encoding derived variables and axiom rules to evaluate them in a symbolic representation. We prove that all encodings are sound and complete, and empirically show that the presented approach outperforms the previous state of the art in costoptimal classical planning with axioms.This work was supported by the German National Science Foundation (DFG) as part of the project EPSDAC (MA 7790/1-1) and the Research Unit FOR 1513 (HYBRIS). The FAI group of Saarland University has received support by DFG grant 389792660 as part of TRR 248 (see https://perspicuous-computing.science)