Dissymmetry Factor Enhancement: A Data-Driven Approach to Unravel the Synthesis Knobs of Chiral 2D Perovskites
Chiral 2D metal halide perovskites (MHPs) hold promise for next-generation spin-optoelectronic applications. While still in early-stage development, significant efforts have focused on optimizing the dissymmetry factor (g), particularly in terms of their circular dichroism (gabs), which quantifies the chiroptical response and is related to spin-polarized optical properties in chiral systems. These materials typically exhibit intrinsically low gabs values (10E-3), with variations spanning two orders of magnitude due to co-dependent structural, morphological, and experimental factors. Here, Pearson correlation analysis identified solvent choice as the primary factor governing gabs variability in chiral 2D MHP thin films based on R-/S-𝛼-methylbenzylammonium. Subsequently, a combination of Pearson correlation, ANOVA, and Gaussian Process Regression revealed that, for acetonitrile-based samples, strain, film thickness, and annealing temperature are the key parameters for optimizing gabs. In contrast, films prepared with higher boiling point solvents exhibit a more complex interdependence of gabs with film texture and excitonic integral intensity. The statistical analysis of the synthesis-chiroptical property relationship provides a foundation for the rational design of chiral 2D MHPs, while also guiding the exploration of alternative chiral cations, compositions, and deposition strategies.
Authors
- Raphael F. Moral (ORCID: https://orcid.org/0000-0002-1844-4035)
- R.N. Nurdillayeva (ORCID: https://orcid.org/0000-0001-9444-737X)
- Maher B. Alghalayini (ORCID: https://orcid.org/0000-0002-4071-0272)
- David P. Fenning (ORCID: https://orcid.org/0000-0002-4609-9312)
- Marcus M. Noack (ORCID: https://orcid.org/0000-0002-7288-4787)
- Tim Kodalle (ORCID: https://orcid.org/0000-0002-8792-9669)
- Paulo E. Marchezi (ORCID: https://orcid.org/0000-0002-7866-5411)
- Carolin M. Sutter‐Fella (ORCID: https://orcid.org/0000-0002-7769-0869)
- Craig P. Schwartz (ORCID: https://orcid.org/0000-0002-4149-2684)
- Do-Kyoung Lee (ORCID: https://orcid.org/0009-0006-8393-3069)
Institutions
- University of Nevada, Las Vegas (US)
- University of Nevada, Reno (US)
- Lawrence Berkeley National Laboratory (US)
- University of San Diego (US)
- Ahmet Yesevi University (KZ)
Publication Details
- Journal
- ChemRxiv
- Published
- 2025-08-25
- DOI
- https://doi.org/10.26434/chemrxiv-2025-ljpc7
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- U.S. Department of Energy
- Office of Science
- Basic Energy Sciences
- Advanced Scientific Computing Research