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Abstract Autonomous experimentation–or self-driving labs–offers a systematic approach to accelerate materials discovery by integrating automated synthesis, characterization, and data-driven decision-making. We present a closed-loop workflow for the on-demand synthesis and structural characterization of colloidal gold nanoparticles, enabling direct mapping from composition to nanoscale structure. Our framework leverages differentiable models of spectral shape to address two central tasks in self-driving labs: (a) phase mapping, or identifying compositional regions with distinct structural behavior; and (b) material retrosynthesis, or optimizing compositions for target structure. Using functional data analysis, we develop a data-driven model with generative pre-training, active learning, and high-throughput experiments to predict spectral responses across composition space. We demonstrate the approach on seed-mediated growth of gold nanoparticles, showcasing its ability to extract design rules, reveal secondary interactions, and efficiently navigate morphology space. Gradient-based optimization of the models enables inverse design, making this a unified platform.
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This content will become publicly available on November 18, 2026
Automated and High-Throughput Phase Separation Control for Supramolecular Polymer Blends Enabled by Machine Learning
Supramolecular polymer blends (SPBs) represent a versatile class of polymers whose morphology directly determines their macroscopic properties. However, rational design of SPBs remains hindered by the lack of predictive models describing how molecular features and intermolecular interactions determine morphology. Here, we report a data-driven high-throughput workflow integrating modular synthesis, robotic sample formulation and processing, automated morphology characterization, and machine learning (ML) for SPBs discovery. Using a plug-and-play modular synthetic strategy, 33 hydrogen-bonding end-functional homopolymer precursors were prepared and orthogonally paired to fabricate 260 SPBs within one day. A custom automated atomic force microscopy (AFM) protocol enabled systematic morphological characterization, producing 2340 images with little human intervention. Average phase separation sizes (e.g. domain spacings) was extracted from processed AFM data using multiple complementary approaches and applied to ML model training. Leveraging the high-throughput sample formation and characterization, a high-quality database was curated for SPBs, allowing training of ML models. Guided by support vector regression (SVR) model, target morphologies of 50, 100, and 150 nm were successfully predicted and experimentally validated. This work demonstrates the potential of coupling high-throughput experimentation with ML to accelerate polymer blends phase discovery, providing one of the first large-scale, experimentally derived datasets specifically designed for supramolecular polymer research.
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- Award ID(s):
- 2229686
- PAR ID:
- 10651741
- Publisher / Repository:
- ChemRxiv
- Date Published:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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