Hierarchical crystal structure prediction of zeolitic imidazolate frameworks using DFT and machine-learned interatomic potentials

Year: 2026

Type: article

Abstract: Crystal structure prediction using machine-learned interatomic potentials enables the high-throughput exploration of the crystal energy landscape of zinc imidazolate (ZnIm 2 ).

Source: Chemical Science

DOI: 10.1039/d6sc04307k

Authors: Yizhi Xu, Jordan Dorrell, Katarina Lisac, Ivana Brekalo, James P. Darby, Andrew J. Morris, Mihails Arhangelskis

Institutions: Ruđer Bošković Institute, University of Warsaw, University of Southampton, University of Birmingham

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: diamond

Funders: Dell EMC, UK Research and Innovation, European Commission, Narodowe Centrum Nauki, Hrvatska Zaklada za Znanost, Infrastruktura PL-Grid, Engineering and Physical Sciences Research Council, Academic Computer Centre Cyfronet, AGH University of Science and Technology

OpenAlex ID: W7120127614