Posters at this year's conference

We introduce a semi-automated workflow and open-source software pyGecko for precise analysis of high-throughput reaction screening without external calibration. This accelerates chemical discoveries and paves the way for downstream machine learning applications.

This work focuses on designing tools to automate microgel synthesis to identify top performing inflammation-targeting microgel formulations for drug delivery.

This study presents an AI-guided and automation-integrated workflow for developing IrRu-based OER catalysts. Multi-Objective AI model and automated synthesis and evaluation systems were employed to optimize catalyst compositions based on activity, stability, and cost.

Rapid protein solution viscosity estimation as a function of concentration. The presented machine learning model combines graph and language components to incorporate both structural and sequential protein features.

We present a modular platform for the development of graph-based EI-MS spectra prediction models, supporting the entire ML lifecycle.

Instead of relying on human intuition to define experimental boundaries, our system learns them on its own—making autonomous labs more flexible, scalable, and truly self-directed.

We present an AI-powered platform that automates the nanoparticle washing process by integrating robotic arms, computer vision, and large language models (LLMs). This innovative system enhances reproducibility and efficiency in chemical experiments by adapting dynamically to visual and cognitive changes in the experimental environment.

DIGIBAT provides automated workflows covering materials synthesis, electrode preparation, coin cell assembly, and electrochemical testing. By leveraging robotics and automated data collection, the facility reduces experimental time while enhancing reproducibility towards scalability. Our goal is to accelerate energy research, shorten the materials discovery timeline, and enable AI-powered innovation in battery and Power-to-X development.

We use several different spectroscopies to monitor chemical reactions in real time. Evaluating all spectra jointly allows us to identify unknown (intermediate) products and control multistep reactions in detail.

This study proposes an inverted configuration where the mortar is mounted on the robotic arm, enabling a single robot to execute continuous grinding and cleaning. In the next phase, integrating this system with characterization tools will move toward a fully autonomous loop, encompassing powder processing, synthesis, and subsequent evaluation.

We developed a machine learning approach that accelerates the discovery of efficient catalysts by using quantum-inspired similarity to reduce computational cost. This method successfully identified a promising new material for clean energy applications.

We developed autonomous robotic experimental setup that can find out the suitable parameter conditions for controlling the coffee ring effect. The system was coupled with AI-driven interface, NIMO (NIMS Orchestration System), to facilitate autonomous experimentation. Additionally, the PDC (Phase Diagram Construction) method was employed to efficiently explore the boundary condition of coffee ring formation.

EvoMPFs are optimized molecular representations for machine learning, derived using an evolutionary algorithm that identifies the most relevant structures in a set of molecules. This enables researchers to make accurate chemical predictions while revealing meaningful patterns in their data.

Current limitations in laboratory robot control systems hinder the automation of complex tasks like powder mixing in fuel cell research, where manual experimentation remains prevalent. This study explores the application of imitation learning to the complex task of powder grinding, aiming to demonstrate its potential for advancing laboratory automation by comparing its performance to existing methods.

This work presents a novel, cost-effective control platform for automated powder dispensing, integrating an analytical balance, a powder dispenser, and a low-cost motor. The system enables robust, sub-milligram-level dispensing performance across various powder types, including free-flowing and highly cohesive materials.

Due to reporting bias, the yield prediction of chemical reactions, still suffers from the lack of negative data reported in literature. Positivity Is All You Need (PAYN) is Python-based framework that tackles this challenge by using positive-unlabeled learning.

We introduce CombineNet, a general-purpose neural network potential incorporated with adaptive dispersion and electrostatic corrections. The model describes long-range intermolecular interactions with physical asymptotic behaviour and corrects the potential energy prediction inherited from density-functional theory reference methods.

A novel human-informed machine learning workflow combining pre-trained models with state-of-the-art computer vision demonstrates efficiency and effectiveness at image-based cell culture analysis, indicating its suitability for integration into an autonomous biological self-driving laboratory platform.

ATLAS is an automated high-throughput facility at Imperial College London, enabling rapid synthesis, sample preparation, and characterisation for materials and molecular discovery. By integrating robotic workflows with fast, data-rich analysis, ATLAS helps researchers in academia and industry accelerate innovation across chemistry and materials science.

Robot arm performs organ-on-a-chip cultures with a speed and precision surpassing human lab-workers.

We explore the benefits of employing autonomous experimentation for rapid and intelligent navigation of the complex and unintuitive alloy design space. Our approach builds a foundation for autonomous experimentation by leveraging both design principles and hardware from industrial automation to enable high-throughput sample printing, transportation, grinding, polishing, cleaning, microscopy, and hardness testing to capture the lifecycle of alloys.

We present progress towards a machine learning approach that predicts  the future state of a battery given the current state and a charging protocol; adapting the charging protocols to the battery state is known to improve battery health. Our approach is specifically designed to be integrated into self-driving labs for accelerated materials discovery, and leverages representations of battery degradation extracted from EIS/transport measurements to make predictions.

We deployed and evaluated a teleoperation system that allows remote human intervention in self-driving laboratories to recover from unexpected errors and maintain smooth operation. By using a 3D mouse to control a robotic arm, our approach improves precision, reduces recovery time, and enhances the resilience of automated experimental workflows.

We are working to integrate an array of tools within a widely accessible laboratory liquid handler to not only prepare samples, but also collect data, take measurements, and perform characterizations. This helps create an all-in-one ecosystem within the liquid handler to perform complete experiment protocols and collect data without the need for human inference.

A platform was developed to automate a purification process called liquid-liquid extraction and using machine learning algorithms to evaluate the quality of extraction.