Work

Modelling silica using MACE-MP machine learnt interatomic potentials

Year: 2025

Type: article

Abstract: species, in agreement with prior DFT and experimental observations. Thus, we assess and demonstrate the suitability of off-the-shelf machine-learned foundation models, based on MACE-MP framework, for modelling silica materials of high importance from earth sciences to electronics and catalysis.

Source: Physical Chemistry Chemical Physics

DOI: 10.1039/d5cp01882j

Authors: Jamal Abdul Nasir, Jingcheng Guan, Woongkyu Jee, Scott M. Woodley, Alexey A. Sokol, C. Richard A. Catlow, Alin-Marin Elena

Institutions: University College London, Rutherford Appleton Laboratory, Research Complex at Harwell, UK Catalysis Hub, Cardiff University, Daresbury Laboratory

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: hybrid

Funders: Engineering and Physical Sciences Research Council, Science and Technology Facilities Council

Awards: CCP5++: Integrating Computer Simulation of Condensed Phases with experiments and data science., Materials Chemistry HEC Consortium (MCC), G243872479, The Materials and Molecular Modelling Hub, G41932317, G5291085235, G5554892693, Particles At eXascale on High Performance Computers (PAX-HPC), G90098195

OpenAlex ID: W4412508563

FWCI: 1.669

Cites: 97

Cited by: 8

Related to: 10