Exploring elastic properties of molecular crystals with universal machine learning interatomic potentials

Kholtobina, Anastasiia; Lončarić, Ivor (2025) Exploring elastic properties of molecular crystals with universal machine learning interatomic potentials. Materials & Design, 254 . ISSN 0264-1275

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Abstract

We benchmarked existing and newly trained universal machine learning interatomic potentials for modeling molecular crystals, particularly their elastic properties. We found that potentials trained on the SPICE dataset provide reasonable predictions of the elastic properties of molecular crystals that are as good as predictions made using density functional theory-based methods. Still, the uncertainty of predictions and difference to experimental values is relatively high (larger than 5 GPa for Young's modulus). We have performed a high-throughput study of the elastic properties of molecular crystals. We have found that some of the molecular crystals show negative linear compressibility and validated our results using density functional theory.

Item Type:	Article
Uncontrolled Keywords:	molecular crystals; elastic properties; machine learning interatomic potentials
Subjects:	NATURAL SCIENCES > Physics NATURAL SCIENCES > Physics > Condensed Matter Physics NATURAL SCIENCES > Chemistry > Physical Chemistry NATURAL SCIENCES > Chemistry > Theoretical Chemistry
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:	23 Sep 2025 12:59
URI:	http://fulir.irb.hr/id/eprint/10011
DOI:	10.1016/j.matdes.2025.114047

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