A fully automated approach to calculate the melting temperature of elemental crystals
Open accessAbstract
The interface method is a well established approach for predicting melting points of materials using interatomic potentials. However, applying the interface method is tedious and involves significant human intervention. The whole procedure involves several successive tasks: estimate a rough melting point, set up the interface structure, run molecular dynamic calculations and analyze the data. Loop calculations are necessary if the predicted melting point is different from the estimated one by more than a certain convergence criterion, or if full melting/solidification occurs. In this case monitoring the solid–liquid phase transition in the interface structure becomes critical. As different initial random seeds for the molecular dynamic simulations within the interface method induce slightly different melting points, a few ten or hundred interface method calculations with different random seeds are necessary for performing a statistical analysis on these melting points. Considering all these technical details, the work load for manually executing and combining the various involved scripts and programs quickly becomes prohibitive. To simplify and automatize the whole procedure, we have implemented the interface method into pyiron (http://pyiron.org). Our fully automatized procedure allows to efficiently and precisely predict melting points of stable unaries represented by arbitrary potentials with only two user-specified parameters (interatomic potential file and element). For metastable or dynamically unstable unary phases, the crystal structure needs to be provided as an additional parameter. We have applied our automatized approach on fcc Al, Ni, dynamically unstable bcc Ti and hcp Mg and employed a large set of available interatomic potentials. Melting points for classical interatomic potentials of these metals have been obtained with a numerical precision well below 1 K.
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The modular, role-specialized architecture is motivated by recent studies showing that distributing reasoning and tool control across multiple collaborating agents improves robustness and reasoning efficiency compared to single-agent systems.
“This modular, rolespecialized architecture is motivated by recent studies showing that distributing reasoning and tool control across multiple collaborating agents improves robustness and reasoning efficiency compared to single-agent systems.[28][29][30]”
From Multi-agentic AI framework for end-to-end atomistic simulations (2026) · cites this paper as [29]
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- Authors
- Zhu, L.-F., Janssen, J., Ishibashi, S., Körmann, F., Grabowski, B., & Neugebauer, J.
- Affiliations
- Max-Planck-Institut für Nachhaltige Materialien (DE)
- University of Stuttgart (DE)
- Corresponding author
- Zhu, Li-Fang
- Venue
- Computational Materials Science
- Publisher
- Elsevier BV
- Published
- 2021-02
- Volume
- 187
- Pages
- 110065
- ISSN
- 0927-0256 (ISSN-L)
- OpenAlex
- W3089505274
- Cited by
- 47
- References
- 46
- Citations by year
- 2026: 10 · 2025: 10 · 2024: 9 · 2023: 9 · 2022: 3 · 2021: 6
- FWCI
- 2.21
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Computational Materials Science (publishedVersion, cc-by)
- MPG.PuRe (Max Planck Society) (submittedVersion, cc-by)
- Research Repository (Delft University of Technology) (submittedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion, cc-by)
- Funders
- Deutsche Forschungsgemeinschaft (405621217)
- Max-Planck-Gesellschaft
- Stichting voor de Technische Wetenschappen (15707)
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek
- Horizon 2020
- European Research Council (639211)
- Topics
- Physical Sciences › Engineering › Mechanical Engineering › Thermodynamic and Structural Properties of Metals and Alloys
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › nanoparticles nucleation surface interactions
- Keywords
- melting point prediction, interface method, elemental crystals, pyiron, workflow automation, interatomic potentials, melting, random seed sensitivity, metastable phases, aluminum, nickel, titanium, magnesium, face-centered cubic metals, body-centered cubic metals, hexagonal close-packed metals
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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