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An ionic compound is stable because of the electrostatic attraction between its positive and negative ions. The lattice energy of a compound is a measure of the strength of this attraction. The lattice energy (ΔHlattice) of an ionic compound is defined as the energy required to separate one mole of the solid into its component gaseous ions. For the ionic solid sodium chloride, the lattice energy is the enthalpy change of the process:

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Updated: Oct 4, 2025

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Unveiling Origins of Mixed Quantum-Well Width Distributions in 2D Ruddlesden-Popper Perovskites via Machine

Svetozar Najman¹, Po-Yu Yang¹, Yi-Xian Yang²

¹Research Center for Applied Sciences, Academia Sinica, No. 128, Section 2, Academia Road, Taipei, 11529, Taiwan.

|September 5, 2025

Summary

Machine learning models reveal that 2D perovskites evolve towards mixed quantum well (QW) widths. This occurs due to the enhanced stability of lower-n QW layers, crucial for designing better optoelectronic devices.

Keywords:

2D perovskite machine learning microstructure multiscale simulation

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Influence of Hybrid Perovskite Fabrication Methods on Film Formation, Electronic Structure, and Solar Cell Performance

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Published on: February 27, 2017

Area of Science:

Materials Science
Chemical Physics
Condensed Matter Physics

Background:

2D lead-halide perovskites offer tunable optoelectronic properties and stability.
Quantum well (QW) width (n) controls properties but mixed distributions hinder device performance.
Understanding QW width distribution dependence on composition and stability is critical.

Purpose of the Study:

To investigate the formation of mixed quantum well (QW) width distributions in 2D perovskites.
To understand the influence of chemical composition and thermodynamic stability on QW width.
To provide guidelines for designing advanced perovskite optoelectronic devices.

Main Methods:

Developed a machine learning (ML)-based energy model, validated with first-principles calculations.
Employed hybrid Monte Carlo simulations for large-scale molecular-level modeling.
Studied 2D perovskites with butylammonium (BA) and phenethylammonium (PEA) spacer cations.

Main Results:

Demonstrated a rapid evolution from homogeneous to energetically favored mixed-phase QW structures.
Identified enhanced thermodynamic stability of low-n layers as the primary driver for mixed phases.
Highlighted the strong affinity of BA/PEA cations to the inorganic PbI6 framework.

Conclusions:

ML-powered multiscale modeling offers deep insights into 2D perovskite microstructures.
Thermodynamic stability, driven by spacer cation interactions, dictates QW width distribution.
Findings guide the rational design of next-generation perovskite optoelectronics.