Construction and Sampling of Alloy Cluster Expansions—A Tutorial

Crystalline alloys and related mixed systems make up a large family of materials with high tunability that have been proposed as the solution to a large number of energy-related material design problems. Because of the presence of chemical order and disorder in these systems, neither experimental efforts nor computational methods alone are sufficient to span the inherently large configuration space. Therefore, fast and accurate models are necessary. To this end, cluster expansions have been widely and successfully used for the past few decades. Cluster expansions are generalized Ising models designed to predict the energy of any atomic configuration of a system after training on a small subset of the available configurations. Constructing and sampling a cluster expansion consists of multiple steps that have to be performed with care. In this tutorial, we provide a comprehensive guide to this process, highlighting important considerations and potential pitfalls. The tutorial consists of three parts, starting with cluster expansion construction for a relatively simple system, continuing with strategies for more challenging systems such as surfaces, and closing with examples of Monte Carlo sampling of cluster expansions to study order-disorder transitions and phase diagrams. Published by the American Physical Society 2024

Authors

Institutions

Publication Details

Journal
PRX Energy
Published
2024-10-17
DOI
https://doi.org/10.1103/prxenergy.3.042001
Citations
5
Primary Topic
nanoparticles nucleation surface interactions
Type
article
Field-Weighted Citation Impact
1.88

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Construction and Sampling of Alloy Cluster Expansions—A Tutorial

Pernilla Ekborg-Tanner, Erik Fransson, Petter Rosander, Paul Erhart
5 citations
PRX Energy
nanoparticles nucleation surface interactions
1.88
article

Construction and Sampling of Alloy Cluster Expansions—A Tutorial

Pernilla Ekborg-Tanner, Erik Fransson, Petter Rosander, Paul Erhart
article en
5 citations

Abstract

Crystalline alloys and related mixed systems make up a large family of materials with high tunability that have been proposed as the solution to a large number of energy-related material design problems. Because of the presence of chemical order and disorder in these systems, neither experimental efforts nor computational methods alone are sufficient to span the inherently large configuration space. Therefore, fast and accurate models are necessary. To this end, cluster expansions have been widely and successfully used for the past few decades. Cluster expansions are generalized Ising models designed to predict the energy of any atomic configuration of a system after training on a small subset of the available configurations. Constructing and sampling a cluster expansion consists of multiple steps that have to be performed with care. In this tutorial, we provide a comprehensive guide to this process, highlighting important considerations and potential pitfalls. The tutorial consists of three parts, starting with cluster expansion construction for a relatively simple system, continuing with strategies for more challenging systems such as surfaces, and closing with examples of Monte Carlo sampling of cluster expansions to study order-disorder transitions and phase diagrams. Published by the American Physical Society 2024

PRX EnergyVol. 3(4)
Chalmers University of Technology (SE)
Chalmers Tekniska Högskola, Vetenskapsrådet, Energimyndigheten
Affordable and clean energy
Openalex Percentile: Top 15%
nanoparticles nucleation surface interactions
1.88
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.