Multicomponent Nanocrystal Superlattice Structure Prediction from First-Principles Validated against Two Decades of Experiments
Abstract Predicting nanocrystal (nanoparticle) assembly into ordered arrays (superlattices) is a grand challenge in materials chemistry that has largely resisted first-principles theories. Here, we extend MOLT-CF, a classical density functional theory, to multicomponent nanocrystal systems and subject it to a rigorous validation by comparing against nearly 300 experiments spanning two decades of literature. We use MOLT-CF to predict stability and phase diagrams for binary superlattices of nanocrystals with linear hydrocarbon ligands, as well as many other fine structural details, for example, lattice constants, packing fractions, ligand structure, and, in the case of (cubic) perovskite nanocrystals, orientation. Significant discrepancies from experiments are noted only for the Li3Bi and Cu3Au superlattices. In the former, we predict that this lattice is stabilized by particle fractionalization, which is supported by a reexamination of experimental data in the literature. In the case of Cu3Au (for which MOLT-CF predicts thermodynamic instability against phase separation into single-component lattices), we propose two alternative candidate structures that reconcile theory with experimental observations. These results establish MOLT-CF as a rigorous platform for the computational design of next-generation functional nanoparticle materials.
Authors
- Yamila A. Perez Sirkin (ORCID: https://orcid.org/0000-0003-2095-6491)
- Leandro L. Missoni (ORCID: https://orcid.org/0000-0002-3131-4606)
- Mario Tagliazucchi (ORCID: https://orcid.org/0000-0003-4755-955X)
- Alex Travesset (ORCID: https://orcid.org/0000-0001-7030-9570)
- Santiago F. Bonoli (ORCID: https://orcid.org/0009-0000-0572-6044)
Institutions
- Iowa State University (US)
- Universidad de Buenos Aires (AR)
- Fundación Ciencias Exactas y Naturales (AR)
Publication Details
- Journal
- Journal of the American Chemical Society
- Published
- 2026-10-14
- DOI
- https://doi.org/10.1021/jacs.6c15291
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Consejo Nacional de Investigaciones Científicas y Técnicas
- Agencia Nacional de Promoción de la Investigación, el Desarrollo Tecnológico y la Innovación
- Division of Materials Research