This talk will present how combining continuous-flow reactors with autonomous experimentation with autonomous experimentation (what the authors call a Fluidic Self-Driving Lab ) can accelerate research in colloidal nanoscience.
Abstract
Discovering and improving new semiconductor nanomaterials made in solutions is often a slow process that relies on trial and error. Traditional methods using batch reactors can be inconsistent, especially with heating and mixing, making it hard to explore all the possible ways to create and process these materials. Even though these nanomaterials have remarkable properties and are widely used in energy and chemical technologies as well as photonic devices, we need better approaches to speed up their discovery and development. Recent advances in reaction miniaturization, automated experiments, in-situ multi-modal characterization, and using machine learning (ML) for experimental planning offer exciting new opportunities to accelerate nanomaterials discovery and development. In my talk, I'll present how combining continuous-flow reactors with autonomous experimentation (what we call a Fluidic
Self-Driving Lab
) can accelerate research in colloidal nanoscience. By breaking down the steps of making nanomaterials and processing into separate modules, using methods that can run up to 100 experiments per minute, and applying ML to help model the processes in real-time and make informed decisions about future experiment(s), we can efficiently navigate complex and high-dimensional experimental spaces. Specific examples will be shared to show how these self-driving fluidic labs can autonomously and precisely create metal halide perovskites, as well as II–VI and III–V semiconductor nanocrystals, reducing the development timeline from more than a decade to just a few weeks.
Discovering new high-performance materials and alloys is often likened to finding a needle in a haystack because the design space of chemical compositions and processing conditions is astronomically large, and the discovery process is laborious and prohibitively costly. This perspective articulates a research agenda for autonomous materials and metallurgy discovery platforms that combine closed-loop machine learning with robotic experimentation and traditional materials engineering precepts to formulate, process, and test candidate materials with reduced human intervention, and to iterate recursively for stepwise optimization. It explores how sequential learning methods, generative modeling, and hybrid model-based strategies can analyze multimodal experimental data and propose new compositions and processing schedules for subsequent experiments. This approach has the potential to considerably accelerate the pace of discoveries, from decades to mere weeks. Representation learning for material formulations, the injection of domain knowledge into decision-making, and the mechatronic design of robotics that can safely execute harsh processing are all explored as essential components in closing the autonomous loop. Surveyed exemplars show order-of-magnitude improvements in experimental throughput and discovery efficiency. Furthermore, the paper proposes a multidimensional framework for classifying and evaluating the autonomy, complexity, and integration levels of self-driving laboratories, intended to give researchers and practitioners a structured approach to benchmarking future developments in the field. The perspective concludes with an analysis of data infrastructure, simulation coupling, robustness, and human-machine collaboration, distilled into a structured research agenda whose pursuit would broaden adoption across energy, aerospace, and other sectors.
B. Mallia, Rossana Caputo· Computing&AI Connect· 0 citations
The intersection of machine learning (ML) and electrochemical research is catalyzing transformative advancements in energy storage and conversion technologies. This review critically examines the role of ML and deep learning (DL) in optimizing electrochemical systems, focusing on batteries, supercapacitors, fuel cells, and sensors. ML-driven approaches facilitate accelerated material discovery, precise property predictions, and enhanced device performance monitoring, surpassing conventional trial-and-error methodologies. Integrating computational materials science, including density functional theory (DFT) and molecular dynamics (MD), with ML enables predictive modelling of electrochemical processes at an unprecedented scale. However, challenges such as data heterogeneity, model interpretability, and computational cost continue to limit widespread adoption. This review identifies key strategies to overcome these barriers, including establishing standardized data repositories, developing hybrid physics-informed ML models, and implementing explainable AI (XAI) for enhanced model transparency. By addressing these challenges, ML has the potential to drive the next wave of breakthroughs in electrochemical energy storage and conversion, accelerating the transition toward a sustainable and energy-secure future.
Unknown authors· Journal of the Electrochemic...· 0 citations
Machine learning (ML) is progressively being integrated into materials science, exhibiting great potential for optimizing chemical synthesis and structural regulation, thereby accelerating intelligent design and efficient exploration of novel materials. Carbon-based emitters (CBEs), as emerging functional materials, have attracted considerable attention due to their tunable photoluminescence properties, abundant precursor sources, and structural versatility. To date, extensive research on CBEs has generated a substantial data foundation, laying the groundwork for ML driven structural screening and property prediction. Although the application of ML in CBEs remains in its early stages compared to that of inorganic semiconductors and organic-inorganic hybrid perovskites, its potential to accelerate material screening and reveal structure-property relationships is becoming increasingly evident. Given the transformative role ML has played in other functional materials, it is expected to drive a paradigm shift in CBEs research from conventional trial-and-error approaches to data-driven, intelligence-guided design, substantially accelerating material discovery and expanding their functional applications. Therefore, this review systematically summarizes recent advances in ML applications in CBEs, focusing on the general ML workflow, property prediction and structural design strategies for organic small-molecules and carbon dots (CDs), and their applications in organic light-emitting diodes (OLEDs), quantum dot light-emitting diodes (QLEDs), information encryption, biomedicine, and sensing. Finally, this review discusses the key challenges currently facing the field and offers perspectives on future directions for ML-driven CBEs research, aiming to provide guidance for the rational design and efficient development of such materials.
Artificial intelligence (AI) and nanotechnology have emerged as two transformative scientific domains whose convergence is accelerating the development of next-generation nanomaterials with enhanced functionality, precision, and sustainability. AI-driven computational intelligence enables rapid material discovery, predictive modelling, autonomous experimentation, and process optimisation, thereby significantly reducing the time, cost, and complexity associated with conventional nanomaterial research. Simultaneously, advances in nanotechnology have expanded the possibilities for engineering materials with exceptional electrical, optical, mechanical, catalytic, and biomedical properties. The integration of machine learning, deep learning, computer vision, and data-driven optimisation with nanoscale material design has opened new avenues for applications in healthcare, energy storage, environmental remediation, electronics, aerospace, and smart manufacturing. Furthermore, intelligent digital platforms facilitate real-time quality assessment, defect prediction, and performance optimisation across the nanomaterial life cycle. Despite remarkable progress, challenges related to data quality, model interpretability, scalability, standardisation, and ethical deployment remain significant barriers to widespread industrial implementation. This paper presents a comprehensive academic investigation into the synergistic relationship between AI and nanotechnology, highlighting recent innovations, emerging methodologies, application domains, current limitations, and future research opportunities that are expected to shape intelligent nanomaterial development for sustainable scientific and industrial advancement.
Amjid Nadeem, Brajesh Kumar Mishra, J. Raja et al.· International journal of com...· 0 citations
Chemical research is no longer confined strictly to the lab bench or trial and error. Artificial
intelligence is transforming the field, helping to predict molecular behavior, find potential designs,
and automate certain aspects of the discovery process, introducing a new kind of intuition. Over
the past decade, advances in machine learning, natural language processing, robotics, and automation
have enabled new areas of research. These are broadening the applications for retrosynthetic analysis,
reaction optimization, and computer-aided synthetic planning. This study examines the evolution of
computer-aided synthesis, describing its development from rule-based approaches to advanced deep
learning and hybrid systems that leverage large datasets. Thus, it focuses on AI platforms that integrate
predictive algorithms with rapidly evolving robotic systems. Such technologies enable rapid
hypothesis generation, reaction screening, and the improvement of synthetic methods. The review
encompasses synthesis analysis tools, recommendation algorithms, and autonomous labs that deliver
discoveries more quickly and minimize waste and environmental impact. It examines current challenges,
such as data scarcity, sporadic reporting, model interpretability, and practical applications.
More broadly, the need for sustainable, collaborative research has increased, and cross-border work
through cloud-based laboratories and shared databases enables chemists worldwide to share resources.
The review identifies beneficial trends and ongoing challenges, with a view to providing opportunities
for AI to make chemistry greener, accelerate discovery, and improve decision-making across academic
and industrial settings. AI is not replacing chemists but rather enhancing creativity and intuition,
bringing together research that traditional methods would never have allowed, on a scale never before
possible without AI.
Rizvee Ahmad Samir, Yu-Meng Zhang, Zi-Shan Xu et al.· Letters in Organic Chemistry· 0 citations
The discovery of advanced alloys capable of withstanding extreme environmental conditions such as high temperatures, intense radiation, corrosive atmospheres, and mechanical stress is critical for applications in aerospace, nuclear energy, deep-sea exploration, and space missions. Traditional experimental and computational approaches to alloy design are often time-consuming and resource-intensive. Recent advances in artificial intelligence (AI) and machine learning (ML) offer powerful tools to accelerate the discovery process by enabling high-throughput screening, property prediction, and design optimization. This paper presents a comprehensive review and methodology for AI-assisted alloy discovery, focusing on the integration of data-driven models with physical principles, high-fidelity simulations, and experimental validation. We highlight successful case studies, discuss the challenges of data scarcity and model interpretability, and propose a framework for closed-loop design that incorporates generative models and active learning. This AI-driven approach represents a paradigm shift toward faster, more cost-effective discovery of next-generation materials for extreme environments.
Venkatesh Iyer, Nandhini Ravi· International Journal of Mod...· 0 citations
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