High-throughput automation and machine learning could turn nanomaterials discovery from slow trial-and-error into a fast, data-driven workflow.
Image Credit: Andrey_Popov/Shutterstock.com
Imagine a Pt-Ni nanoparticle that can split water into hydrogen and oxygen with remarkable efficiency, or a perovskite quantum dot that emits bright light with minimal energy loss. For such nanomaterials, just a small tweak in size, shape, composition, or surface chemistry can dramatically change performance.
To optimize nanomaterial synthesis, simultaneous tuning of multiple parameters is required, a process that is slow and inefficient with traditional trial-and-error methods.
As nanomaterials grow more complex and multifunctional, conventional synthesis approaches struggle to keep up. Researchers frequently encounter challenges such as poor reproducibility, limited control over size and shape, broad particle size distributions, and hidden interactions between multiple synthesis variables.
Each experiment consumes time, materials, and effort, making systematic optimization both costly and inefficient. These limitations have created an urgency for faster, smarter strategies in nanomaterials development.
High-throughput approaches are a powerful solution. By combining automated synthesis with rapid characterization and data-driven analysis, researchers can create and evaluate hundreds or even thousands of material variations in parallel.
This strategy enables faster identification of optimal compositions, reveals trends that isolated experiments might miss, and allows experimental strategy to be adapted in real-time rather than relying solely on intuition. In effect, nanomaterials discovery is transformed from a slow, linear process into an accelerated and intelligent workflow.1
Here, we review recent advances in high-throughput synthesis, characterization, and optimization strategies for nanomaterials, highlighting their potential and current challenges for industrial applications.
Get all the details: Grab your PDF here!
High Throughput Nanomaterial Synthesis
High-throughput nanomaterial synthesis has advanced rapidly through the integration of automation, combinatorial chemistry, and machine learning. Robotic liquid handlers and microfluidic reactors now enable hundreds to thousands of parallel experiments, facilitating the rapid and reproducible exploration of complex synthesis parameter spaces.
These approaches are widely applicable to metal and semiconductor nanoparticles, quantum dots, perovskites, two-dimensional materials, and metal-organic frameworks.2
A key advance here is the development of closed-loop robotic systems that integrate real-time characterization with data-driven decision making. For example, Cronin et al. developed an autonomous flow reactor using real-time UV-Vis spectroscopy and machine learning (ML) to explore gold nanoparticle (AuNP) synthesis.
In ~1,000 automated experiments, the system pinpointed five distinct AuNP categories and optimized their optical properties, achieving up to 95 % shape yield. The study quite clearly demonstrates how ML can efficiently navigate complex synthesis landscapes with minimal human input.3
Similarly, the “Artificial Chemist” platform created by Epps et al. combined a high-throughput microfluidic reactor with ML selection to tune inorganic perovskite quantum dot (QD) synthesis.
The robotic 'chemist' autonomously identified 11 tailored perovskite QD compositions (band gaps 1.9-2.9 eV) in just 30 hours, using less than 210 mL of reagents, matching target emission wavelengths with <1 meV error.
This work, too, describes the possibility of precision, speed, and material efficiency that can be achieved through autonomous experimentation.4
Image Credit: sergey kolesnikov/Shutterstock.com
Beyond discovery, ML also enables real-time optimization of synthesis conditions. Generative Pre-trained Transformer (GPT) models combined with search algorithms have been used to retrieve literature synthesis recipes and iteratively refine nanoparticle growth parameters.
These systems achieved precise control over the optical responses of gold nanorods in ~735 trials. They generated monodisperse Au/Ag nanocubes in ~50 trials, with spectral variance of under 3 nm across repeats, frequently outperforming conventional strategies.5
As high-throughput synthesis platforms generate increasingly large and complex nanomaterial libraries, rapid and scalable characterization becomes essential. Advanced in situ and high-throughput characterization techniques are therefore critical for validating structure, chemistry, and functionality, enabling fully data-driven nanomaterials research.
High-Throughput Characterization and Data Analysis
High-throughput synthesis must be paired with fast, automated characterization. Modern platforms integrate inline sensors such as UV-Vis, Raman, and XRD, as well as portable assays. For example, Cronin’s automated AuNP reactor included an inline UV-Vis for feedback, and the A Lab platform coupled each powder synthesis to rapid X-ray diffraction.
Mobile-robot labs have demonstrated the shared use of bench instruments, with robots using LC-MS and NMR to analyze reactions on the fly, applying rule-based and ML-based decision logic to select subsequent experiments. Characterization is now becoming as automated as synthesis, enabling live loop closure.
ML in Data Analysis
On the data side, ML tools automatically interpret large datasets from nanoscale characterization. Advanced algorithms can map phases in high-throughput XRD or deconvolute spectral signatures across combinatorial libraries. Neural-network methods and domain-knowledge-encoded solvers have begun to automate phase identification in compositional spreads.
In microscopy and spectroscopy, pattern recognition and big-data analytics accelerate image processing, such as counting nanoparticle shapes or extracting QD emission peaks from thousands of single-particle spectra. These AI-enabled analysis pipelines enable the rapid conversion of vast data generated by high-throughput experiments into insights, such as composition-structure-property relationships, without relying on human bottlenecks.[6]
Saving this article for later? Grab a PDF here.
Autonomous Materials Labs
Beyond individual reactors, fully integrated autonomous labs are emerging. A recent Nature report describes “A Lab,” a robotic facility for solid-state inorganic synthesis. Drawing on ab initio databases and text-mined literature, it proposes synthesis recipes, then mills, reacts, and XRD-characterizes powders via robotics and ML-driven active learning.
In 17 days, it realized 41 novel oxide and phosphate compounds (out of 58 targets identified computationally) without human intervention. This demonstrates how coupling historical data and AI planners with robots can accelerate the discovery of crystalline materials.
Mobile robotic labs have also been shown to perform exploratory synthesis in “human-like” ways. One study used autonomous mobile robots outfitted with NMR and LC-MS (sharing equipment with human researchers) to screen diverse reactions and make decisions based on multimodal analytics. These systems illustrate the potential of fully autonomous laboratories to accelerate materials discovery.[7]
What is the Future of Autonomous Manufacturing for Nanomaterials?
Recent advances signal a transition toward self-driving nanomaterials laboratories, where synthesis, characterization, and decision-making are integrated in closed-loop, data-driven workflows. This approach is especially critical for advanced nanomaterials, where large parameter spaces and subtle structure-property relationships limit conventional discovery.
However, challenges remain. Data standardization and interoperability across automated platforms are insufficient, restricting reproducibility and ML model transferability. High-quality training data are scarce for complex nanomaterials, particularly under operando conditions relevant to energy applications.
Integration of advanced characterization techniques, such as synchrotron and neutron-based methods, into high-throughput pipelines remains technically demanding.
From an industrial perspective, scalability of laboratory-optimized synthesis, robustness of autonomous systems, regulatory acceptance of AI-driven decisions, and intellectual property protection are major barriers.
Addressing these challenges through standardized digital infrastructure, physics-informed machine learning, and hybrid human-AI workflows will be essential for translating high-throughput nanomaterials discovery into reliable, industry-ready platforms.
References and Further Reading
- Chan, E.M., et al., Reproducible, High-Throughput Synthesis of Colloidal Nanocrystals for Optimization in Multidimensional Parameter Space. Nano Letters, 2010. 10(5): p. 1874-1885.
- Clayson, I.G., et al., High throughput methods in the synthesis, characterization, and optimization of porous materials. Advanced Materials, 2020. 32(44): p. 2002780.
- Jiang, Y., et al., An artificial intelligence enabled chemical synthesis robot for exploration and optimization of nanomaterials. Sci Adv, 2022. 8(40): p. eabo2626.
- Epps, R.W., et al., Artificial Chemist: An Autonomous Quantum Dot Synthesis Bot. Adv Mater, 2020. 32(30): p. e2001626.
- Gao, F., et al., A chemical autonomous robotic platform for end-to-end synthesis of nanoparticles. Nature Communications, 2025. 16(1): p. 7558.
- Dai, T., et al., Autonomous mobile robots for exploratory synthetic chemistry. Nature, 2024. 635(8040): p. 890-897.
- Szymanski, N.J., et al., An autonomous laboratory for the accelerated synthesis of novel materials. Nature, 2023. 624(7990): p. 86-91.
Disclaimer: The views expressed here are those of the author expressed in their private capacity and do not necessarily represent the views of AZoM.com Limited T/A AZoNetwork the owner and operator of this website. This disclaimer forms part of the Terms and conditions of use of this website.