Abstract
In silico materials design has become a widely accepted supplement to the experimental trial-and-error approach. Computation-ready, experimental (CoRE) crystal structures are commonly the foundation for creating high-throughput screening workflows to identify optimal compounds, with metal–organic frameworks and covalent organic frameworks being prominent examples. At the same time, data-driven studies devoted to two-dimensional (2D) hybrid organic–inorganic perovskites (HOIPs)—emerging photovoltaics materials—are hindered by the lack of consistently curated datasets. Here we present the CoRE 2D-HOIP database, a collection of 2D HOIP crystal structures that are readily available for atomistic simulations and machine learning. In addition, density functional theory calculations were carried out to obtain thermodynamic and electronic properties, including formation energy, energy above the convex hull, band gap, and electron effective mass. We also implemented a series of graph neural networks to approximate computational and experimental quantities, whereas machine learning interatomic potential for HOIP modeling was developed by finetuning an equivariant neural network originally trained on inorganic compounds. The publicly shared data and models constituting the CoRE 2D-HOIP database are meant to advance the rational design of 2D HOIPs via establishing structure–property relationships and benchmarking machine learning algorithms.
Supplementary materials
Title
Supplemental Information
Description
Document S1. Figures S1–S10
Actions
Supplementary weblinks
Title
CoRE 2D-HOIP DB: Computation-ready, experimental database of two-dimensional hybrid organic–inorganic perovskites
Description
The repository contains the following data associated with the Computation-Ready, Experimental DataBase of two-dimensional Hybrid Organic–Inorganic Perovskites (CoRE 2D-HOIP DB):
cifs folder — Crystallographic Information Files (CIFs) generated within the pymatgen library from the outputs of geometry optimization at the DFT-PBE level of theory
trajectories folder — full optimization trajectories (at the DFT-PBE level of theory) generated within the ase.io.trajectory module
properties.csv file — DFT-computed and experimental property values in comma-separated format
id — database identifier related to CSD refcode
bg_pbe — DFT-computed band gap (PBE functional)
bg_r2scan — DFT-computed band gap (r²SCAN functional)
bg_exp — experimental band gap
e_form — DFT-computed formation energy
e_hull — DFT-computed energy above the convex hull
eff_mass_curv — decimal logarithm of DFT-computed (aka curvature) electron effective mass
eff_mass_opt — decimal logarithm of DFT-computed (aka optical) electron effective mass
Actions
View Title
MACE-2dHOIP-0 models: domain-specific machine learning interatomic potentials for two-dimensional hybrid organic–inorganic perovskites
Description
The repository contains the MACE-MP-0 models fine-tuned on the optimization trajectories of two-dimensional hybrid organic–inorganic perovskites. The model files are compatible with mace.ase interface.
Actions
View