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

Institutions

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

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Multicomponent Nanocrystal Superlattice Structure Prediction from First-Principles Validated against Two Decades of Experiments

Yamila A. Perez Sirkin, Leandro L. Missoni, Mario Tagliazucchi, Alex Travesset et al.
Journal of the American Chemical Society
Machine Learning in Materials Science
article

Multicomponent Nanocrystal Superlattice Structure Prediction from First-Principles Validated against Two Decades of Experiments

Yamila A. Perez Sirkin, Leandro L. Missoni, Mario Tagliazucchi, Alex Travesset, Santiago F. Bonoli
article en

Abstract

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.

Journal of the American Chemical Society
Iowa State University (US), Universidad de Buenos Aires (AR), Fundación Ciencias Exactas y Naturales (AR)
Consejo Nacional de Investigaciones Científicas y Técnicas, Agencia Nacional de Promoción de la Investigación, el Desarrollo Tecnológico y la Innovación, Division of Materials Research
Openalex Percentile: Top 28%
Machine Learning in Materials Science
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.