Posters at this year's conference

Researchers at Monash University in Australia have built a novel robotic platform, capable of making and evaluating new thin film solar cell materials in an autonomous workflow, without human intervention. This platform is part of the Australian Centre for Advanced Photovoltaics.

Can a $4 device make automated experiments tick with guaranteed precision? And can an idea in programming languages transform this hardware capability into an accessible tool for everyone to tinker with?

We have developed an automated flow system for polymer synthesis that can be controlled remotely and continuously monitored in real time using in-line NMR, IR, and UV-Vis spectroscopy. This setup enables the generation of large datasets for machine learning, accelerating the discovery of new polymer materials.

We developed a non-empirical kinetic energy functional with significantly improved energies and densities in the framework of orbital-free density functional theory (DFT). Its accuracy and its cheap quadratic computational time complexity promises a path toward accelerated exploration of molecules/materials much larger than those conventional Kohn-Sham DFT can afford.

A novel comprehensive platform for the discovery and optimization of electrochemical processes, with applications in analytical and synthetic chemistry.

Mixtures of molecules are integral to our daily experiences: from the perfumes we smell to the remedies

This project demonstrates a fully automated platform for fabricating and characterizing reverse-osmosis (RO) membranes using the nonsolvent-induced phase separation (NIPS) method. By integrating robotic solution preparation, casting, and compression-based mechanical testing, the system enables high-throughput exploration of fabrication parameters and provides rapid feedback on membrane structure and performance.

Flexible automation allows lab systems to adapt to changing experiments. We used this approach to convert a solar cell lab into one that transforms CO₂ into fuel.

Sorting through thousands of images to identify tiny insects takes immense time. We're developing cutting-edge AI that automatically detects small arthropods in bulk photos, dramatically speeding up research. This technology helps scientists analyze massive datasets, like those from the global LIFEPLAN project, leading to a faster, better understanding of Earth's biodiversity.

In this work we discuss our process for introducing automation to the modeling, fabrication and characterization of organic thin film transistors. We expand on how this has enabled new experiments in our research, such as environmentally controlled stress testing.

PEGKi orchestrates multiple parallel-run and concurrent autonomous labs, using gamified skill assessment and meta-learning to dynamically assign optimization policies and rapidly discover a target chemical mixture.

We've developed a high-throughput platform combining rapid catalyst synthesis and advanced evaluation methods, including machine vision analysis, to efficiently discover high-performance catalysts for producing clean hydrogen from formic acid. Integrated with artificial intelligence, this approach significantly accelerates sustainable catalyst development.

We developed a machine learning tool that predicts how long-acting injectable drugs release over time, helping researchers create better formulations faster. The model provides mechanistic insights into release behavior and demonstrates robust predictive accuracy

We show how an old AI method for making money on slot machines can cut the supercomputing costs of designing clean energy materials.

Using a carbon nanotube synthesis research robot coupled with an artificial intelligence to design experiments, we simultaneously optimized both the overall yield of material and controlled the diameter of the resulting nanotubes. We show that yield and diameter control and yield are independent outcomes and no sacrifice in yield is necessary for diameter control.

We use a smart AI method to quickly and precisely extract key information from scientific data, helping researchers characterize and discover sustainable materials faster and more efficiently.

This study developed a real-time automated workflow to detect and correct noisy data in self-driving labs using k-nearest-neighbour imputation and statistical evaluation methods. Systematic benchmarking across training data sizes and noise scenarios reveals clear performance limits, offering practical guidelines for maintaining data integrity in high-throughput discovery.

We have developed Auto-SEMEDS, a fully automated software that enables accurate, high-throughput composition analysis of powder materials using SEM-EDS. By refining SEM-EDS quantification methods and integrating machine learning analysis, Auto-SEMEDS enhances powder characterization, making it faster, more reliable, and ideal for self-driving laboratories.

The integration of Bayesian optimization with high-throughput microbial phenotyping platform enables the efficient design of synthetic microbial cultures tailored for real-world industrial applications.

HeinSight is a computer vision system that enables real-time monitoring of physical changes and provides feedback control for autonomous experimental work.

We demonstrate a rapid, automated material characterization tool capable of measuring all 3 figures of merit from photovoltaics to inform device quality, without the need to fully complete the fabrication process.

We developed a powerful platform using patient-derived leukemia stem cells that mimics how cancer stem cells behave and resist treatment. This platform helps researchers discover and test new features that target the root of cancer relapse, with the goal of creating more effective treatments for leukemia and other hard-to-treat cancers originating from cancer stem cells.

Deeper understanding of chemical processes is fundamental towards streamlining production across a wide range of industries. In this work we present a method for selectively monitoring different positions within an experimental sample and provide case-studies to show its utility in many different stages of chemical development.

This study explores how machine learning can optimize 3D printing parameters for polymer materials, specifically poly(caprolactone) (PCL). By analyzing 800 samples with profilometry and stereomicroscopy, we demonstrate how predictive models can speed up the iteration process, improving print quality and accelerating material innovation in additive manufacturing. Our findings highlight an efficient and scalable approach for accelerating innovation in the field.

Our self-driving lab revolutionizes battery materials research by using automation and AI to quickly and efficiently discover advanced cathode materials. By automating the entire process—from design and synthesis to testing and analysis—we significantly speed up the development of cutting-edge lithium-ion batteries, making them more effective and reliable.