Thermosalient Phase Transitions from Machine Learning Interatomic Potential

Mladineo, Bruno; Lončarić, Ivor (2024) Thermosalient Phase Transitions from Machine Learning Interatomic Potential. Crystal Growth & Design, 24 (20). pp. 8167-8173. ISSN 1528-7483

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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.

Item Type:	Article
Uncontrolled Keywords:	machine learning interatomic potentials
Subjects:	NATURAL SCIENCES > Physics NATURAL SCIENCES > Physics > Condensed Matter Physics
Divisions:	Theoretical Physics Division
Projects:	Project title Project leader Project code Project type Povećanje prostorne i vremenske skale modeliranja materijala iz prvih principa pomoću strojnog učenja-ExtMatModelML Ivor Lončarić UIP-2020-02-5675 HRZZ
Depositing User:	Ivor Lončarić
Date Deposited:	03 Feb 2025 11:20
URI:	http://fulir.irb.hr/id/eprint/9524
DOI:	10.1021/acs.cgd.4c00905

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