Work

Cluster expansion toward nonlinear modeling and classification

Year: 2025

Type: article

Abstract: A quantitative first-principles description of complex substitutional materials such as alloys is challenging due to the vast number of configurations and the high computational cost of solving the quantum mechanical problem. Therefore, materials properties must be modeled. The cluster expansion (CE) method is widely used for this purpose, but it struggles with properties that exhibit nonlinear dependencies on composition, often failing even in a qualitative description. By looking at CE through the lens of machine learning, we resolve this severe problem and introduce a nonlinear CE approach, yielding extremely accurate and computationally efficient results as demonstrated by distinct examples.

Source: edoc Publication server (Humboldt University of Berlin)

DOI: 10.18452/34632

Authors: Stroth, Adrian, Draxl, Claudia, Rigamonti, Santiago

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W7080577855

FWCI:

Cites:

Cited by:

Related to: