TL;DR
A tailored machine learning pipeline for predicting and interpreting the stability of OFET devices is proposed, demonstrating the promise of ML in enhancing both predictive accuracy and mechanistic insight in organic electronics, enabling the rational design and rapid screening of stable OSCs and OFETs, and exemplifying a broader shift toward data-driven methodologies in materials research and device engineering.
This work establishes a data-driven and experimentally grounded framework for predicting mobility levels and carrier transport types in polymer OFETs, directly bridging machine learning screening with device-level validation.
Organic solar cell (OSC) performance arises from coupled material, electronic, processing, and morphology-dependent mechanisms, yet the transferability of these relationships across heterogeneous literature-derived datasets remains uncertain. Here, interpretable machine learning (ML) was applied to a curated dataset of...
Here, reported ML-assisted studies on MXene prediction and design are organized by target property and workflow, and ML–DFT screening, graph neural networks, uncertainty quantification, active learning, and interpretability are examined.
A semiempirical extended tight-binding approach (GFN1-xTB) is employed to compute the electronic properties of a dataset of MOFs, and it is shown that GFN1-xTB approximates MOF band gaps well, as compared to semilocal DFT.
A robust machine learning framework is developed designed to establish correlations between molecular structure and device performance and was used to predict new donor–acceptor pairs with PCEs above 20% and identify prospective candidates for further experimental validation.
We present the first experimental machine learning (ML)-enabled Process-Technology Co-Optimization (PTCO) framework for optimizing 2D transition metal dichalcogenide (TMD) FET fabrication directly from statistically meaningful experimental data rather than pure simulation data. We first introduce a transition voltage m...
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