Posters at this year's conference

We utilize dimensionality reduction techniques to understand the degeneracy nature of EIS data. This tool can be implemented in different domains to diversify the EIS dataset which will be further used for training ML models.

LISA is a robotic lab assistant that helps scientists organize and prepare their experiments, automatically tracks inventory in real-time, and reduces repetitive tasks- allowing researchers to focus more on the science itself.

Recent advances in machine learning for materials show that even simple representations, like chemical formulas without structural details, can predict properties effectively, challenging traditional physics-based intuition. This work introduces a tomographic framework based on information theory to clarify structure-property relationships and evaluates property-augmented representations across various prediction tasks.

We developed a low-cost robotic system with tactile sensors that gives robots human-like dexterity to precisely handle both rigid and flexible objects, such as wires and tubes, in complex chemical experiments. This brings us closer to fully automated labs for accelerating material discovery  without the need for expensive equipment.

Automating unknown chemical reactions is challenging for multiple reasons. One challenge is to find out when a reaction is finished if something is happening at all. We developed a toolbox for spectroscopic analysis that unlocks new insights without requiring prior in-depth knowledge about the chemistry.

The objective of this study is to use a Deep Convolutional Neural Network (CNN) to denoise computed tomography (CT) scan images in order to segment crack structures and, subsequently, gain a better understanding of 2D and 3D crack propagation in various cementitious-based materials.

We developed a workflow that combines 3D molecular structures with quantum chemistry to enhance Bayesian optimization of chemical reactions, improving performance by up to 35% over traditional ligand representations.

We are creating an automated laboratory workflow that combines self-driven exploration with hyperspectral Raman and photoluminescence imaging. This allows rapid mapping of carbon nanotubes and polymer blends, accelerating functional materials characterization with minimal human intervention.

We develop a federated learning method that utilizes multi-objective optimization to enhance the training of distributed ML models on decentralized laboratories. To support real-world use, we also built a downloadable app that enables decentralized labs to deploy and train models.

Discover the future of lab automation with LADS OPC UA. This powerful new standard uses robust industrial technology (OPC UA) to seamlessly integrate diverse lab equipment and connect your lab to enterprise systems, demonstrating effortless multi-vendor control for accelerated research and development.

This project focuses on extracting chemical compound images from Google Patents and converting them into the SMILES format. Due to the lack of an official API for Google Patents, web scraping techniques were employed to collect images convert to SMILES and add to drug-like compound database

We present a framework that combines reasoning-capable language models with robotic simulations to automatically design and validate laboratory experiments. By integrating with Argonne National Laboratory's MADSci platform, our solution enables fully closed-loop autonomous laboratories that can explore any viable experiment within equipment constraints, dramatically expanding the potential search space for scientific discovery.

We developed Robotic Autonomous Imaging Surface Evaluator (RAISE), a fully closed-loop automated system that uses robotics and machine vision to perform contact angle measurements, capturing how liquids interact with surfaces. This tool speeds up the discovery of new materials and formulations by eliminating human error and running experiments on its own.

We report on the autonomous synthesis of molecular machines with real-time control for reaction monitoring and purification within the XDL programming language. By streamlining and standardizing complex syntheses, we improve reproducibility and reliability whilst enabling broader automation in chemistry thus freeing researchers to pursue more ambitious and exploratory work.

We propose an active learning method for drug discovery, acquiring labels for some samples and training a model to predict the rest. Our models prune the most difficult examples from the target set to achieve high accuracy and reduce experimental costs.

Energy transfer photocatalysis provides a versatile platform for the efficient, sustainable and rapid construction of complex molecular scaffolds. We leverage data-driven exploration and mechanistic investigation strategies to accelerate the discovery of novel synthetically valuable transformations and to better understand chemical reactivity in this emerging catalytic mode.

Leveraging automated fabrication and testing, we discovered highly active and stable electrocatalysts for producing green hydrogen

HiWAY-2-MAT is a synthesis and high-throughput characterisation platform, and one of 17 platforms being set up across France as a part of a large scale project to include automation, machine learning, and artificial intelligence in materials design. Here I describe how the platform is set up and designed, and more broadly how it fits into the larger framework of cooperative, independent platforms.

This poster introduces El Agente and El Agente Q, a multi-agent system designed to democratize quantum chemistry workflows through natural language interaction. Built on large language models, it automates complex tasks like molecular modeling, analysis, and error recovery, making cutting-edge chemistry more accessible, efficient, and adaptable for students, researchers, and other aficionados. To learn more about El Agente or pre-register to test our alpha version, visit our website www.elagente.ca.

We’ve developed an AI-driven model that helps discover high-performance fluid mixtures by learning how different molecules interact. By combining graph neural networks and Bayesian optimization, our approach accelerates the search for optimal formulations in applications like drug delivery, thermal management, and advanced materials.

The development of a self-driving MEA electrolyzer platform dramatically accelerates the discovery and optimization of CO₂ electroreduction systems by enabling autonomous, high-throughput experimentation with real-time performance feedback.

SOKE Graph is an AI-powered tool that helps researchers quickly find and organize relevant information from scientific papers about green hydrogen and clean energy. It uses smart prompts and a structured knowledge graph to highlight key concepts, saving time and making discoveries faster and easier.

We introduce ivoryOS, an open-source orchestrator for Python-based self-driving laboratories (SDLs) that automatically generates a drag-and-drop web interface from exposed SDL operations. Its plug-and-play architecture and low-code interface make it easy to share, adapt, and scale across labs—streamlining SDL development and operation for researchers.

We used artificial intelligence to design new drug-like molecules that target ABL1, a protein involved in leukemia. Our approach helps speed up the discovery of potential treatments, especially for drug-resistant cancer mutations.

Our work evaluates state-of-the-art universal machine learning potentials on structural optimization, molecular dynamics (MD), and bulk modulus and heat capacity predictions of metal-organic frameworks, a class of highly porous materials with potential applications in carbon capture, energy storage, and catalysis.