Work

Thermosalient Phase Transitions from Machine Learning Interatomic Potential

Year: 2024

Type: article

Abstract: We developed an accurate machine learning interatomic potential for the thermosalient molecular crystal N -2-propylidene-4-hydroxybenzohydrazide. This crystal exhibits one of the largest mechanical responses during its thermosalient phase transition. Leveraging the speed of our developed potential, we performed Gibbs free energy calculations that successfully predict phase transitions in good agreement with experimental observations. Additionally, our model accurately captures the phenomenon of negative linear thermal expansion preceding the thermosalient phase transition. We show that the energy barrier exists at phase transition temperature and that this energy is purely elastic, elucidating the physical reasons for the thermosalient effect.

Source: Crystal Growth & Design

DOI: 10.1021/acs.cgd.4c00905

Authors: Bruno Mladineo, Ivor Lončarić

Institution: Ruđer Bošković Institute

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: green

Funders: Hrvatska Zaklada za Znanost, European Regional Development Fund

Awards: G4490468271, Extending length and time scales of first principles material modelling via machine learning