ArticleAdvanced materials (Deerfield Beach, Fla.)2026
Data-Driven Design of Self-Assembled Monolayers for High-Efficiency Perovskite Solar Cells.
Article in Advanced materials (Deerfield Beach, Fla.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
Self-assembled monolayers (SAMs) are pivotal for boosting the performance of perovskite solar cells (PSCs). Yet, the intricate link between molecular structure and device efficiency hinders rational SAM design. Here, we introduce a data-driven strategy that leverages a curated dataset of reported SAMs and their PSC efficiencies, with molecular structures encoded into three distinct segments: anchor group-linker-head group. Based on this fragment-encoding framework, our strategy focuses on the recombination of fragment units, rather than unconstrained de novo molecular design. Using ensemble learning and SHapley Additive exPlanations (SHAP) interpretability within a cross-validated framework, we pinpointed the head group as the dominant performance driver. This insight guided the construction of an expanded molecular library by recombining high-value fragments identified from the curated database. Virtual screening of this library then yielded a synthetically accessible SAM molecule with top-predicted efficiency, namely S1. Experimental validation revealed that S1 forms a compact, ordered monolayer on NiO
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.