icet - A Python library for constructing and sampling alloy cluster expansions

Alloy cluster expansions (CEs) provide an accurate and computationally efficient mapping of the potential energy surface of multi-component systems that enables comprehensive sampling of the many-dimensional configuration space. Here, we introduce \textsc{icet}, a flexible, extensible, and computationally efficient software package for the construction and sampling of CEs. \textsc{icet} is largely written in Python for easy integration in comprehensive workflows, including first-principles calculations for the generation of reference data and machine learning libraries for training and validation. The package enables training using a variety of linear regression algorithms with and without regularization, Bayesian regression, feature selection, and cross-validation. It also provides complementary functionality for structure enumeration and mapping as well as data management and analysis. Potential applications are illustrated by two examples, including the computation of the phase diagram of a prototypical metallic alloy and the analysis of chemical ordering in an inorganic semiconductor.Comment: 10 page

Dynamical Accuracy of Water Models on Supercooling

Molecular dynamics (MD) simulations are commonly used to explore the structural and dynamical properties of supercooled bulk water in the so-called "no man\u27s land" (NML) (150-227 K), where crystallization occurs almost instantaneously. This approach has provided significant insight into experimentally inaccessible phenomena. In this paper, we compare the dynamics of simulations using one-, three-, and four-body water models to experimentally measured quasielastic neutron scattering spectra. We show that the agreement between simulated and experimental data becomes substantially worse with a decrease in temperature toward the deeply supercooled regime. It was found that it is mainly the nature of the local dynamics that is poorly reproduced, as opposed to the macroscopic properties such as the diffusion coefficient. This strongly implies that the molecular mechanism describing the water dynamics is poorly captured in the MD models, and simulated structural and dynamical properties of supercooled water in NML must be interpreted with care

Quasi Elastic Neutron Scattering model library

This paper reports on the development of a collection of dynamical models of one-dimensional peak profile functions used to fit dynamic structure factors S (Q, ħω) of Quasi Elastic Neutron Scattering (QENS) data. The objective of this development is to create a maintainable and interoperable Python library with models reusable in other projects related to the analysis of data from Quasi Elastic Neutron Scattering experiments. The ambition is that the library also will serve as a platform where scientists can make their models available for others. We illustrate how the library can be used by newcomers to the field as well as by experts via different examples. These examples, provided as Jupyter notebooks, show how the QENS models can be integrated in the whole QENS data processing pipeline