Posters at this year's conference

We've spent 10 years on modernizing retrosynthesis tools. Chemical.AI's reaction informatics platform now supports human chemists and robotics to conduct synthesis planning, optimize reaction conditions, predict impurities, and so on.

To accelerate the discovery of useful materials, we need to minimize the time and resource consumption associated with running large number of experiments or simulations. Our work comprehensively surveys approaches to experimental planning, providing actionable insights into maximally data-efficient approaches for constructing ML material property prediction models.

We present our progress in establishing two robotic laboratories, integrated fully with Argonne National Laboratory’s Workflow Execution Interface (WEI) automation software, to support an expanding portfolio of autonomous and automated biology workflows. These laboratories facilitate a range of biological applications, from basic molecular biology to bacterial growth monitoring, and will enable the design of novel peptides, proteins, and metabolic pathways for multiple biological applications.

Utilizing a variety of PATs to optimize and automate the synthesis of model predicted ligands.

The urgent need for sustainable energy solutions has positioned green hydrogen, produced through photocatalytic water splitting using sunlight, as a critical component in addressing the global climate crisis. While this carbon-free energy carrier holds immense promise, its widespread adoption hinges on overcoming significant materials science challenges, particularly in developing photocatalysts that combine high efficiency, stability, and scalability. Recent advances have revealed spin-polarized states in ferromagnetic semiconductors as a transformative avenue for optimizing photocatalytic systems, offering new mechanisms to enhance charge carrier dynamics and reaction kinetics

We are building a self-driving lab that uses automation, miniaturization, and laser spectroscopy to generate critical data to accelerate the design of sustainable chemical processes - reducing cost, time, and reliance on manual work.

This study presents a self-driving laboratory (SDL)-powered discovery of fluorene-based organic laser molecules, integrating virtual screening with automated synthesis and characterization. A total of 52 candidates were experimentally validated, including green-yellow-orange-red-NIR emitters with promising optical properties for future optoelectronic applications.

This talk or poster presents an accelerated high-throughput workflow for CO2 electrocatalyst discovery, integrating robotic synthesis, automated characterization, and a parallelized CO2 electrolyzer platform. We focus on accelerating multi-element catalyst screening to target C3 hydrocarbon production.

Our work incorporates AI to discover and design gold nanoparticles to act as highly sensitive chemical sensors in a variety of health and environmental molecular sensing applications.

We use generative AI to build 3D crystal structures of MOFs directly from PXRD patterns and synthesis precursors—bridging the gap between lab data and material discovery.

Robotic Autonomous Imaging Surface Evaluator tailored for ocular devices (RAISE-Vision) integrates customized liquid handling and computer vision to perform repetitive automated contact angle measurement on curved hydrogel surfaces, standardizing wettability analysis for the ocular device field.

By integrating the robotic synthesis, high-throughput crystal structure characterization and machine learning, we developed a model capable of predicting the synthesizability and phases of mixed-metal oxalate solid solutions to accelerate research in materials development based of metal oxalates.

This work develops a photoluminescence-based metric that connects key stability properties of 3D mixed perovskites across different form factors: from quick-and-dirty drop cast samples, to genuine, industry-quality thin-films.

We developed a self-driving laboratory system that autonomously explores new Li-ion conductor thin films. By combining sputtering synthesis and Bayesian optimization, our system identified a new amorphous thin film with one magnitude higher ionic conductivity compared to starting compound.

The Huang group from the University of Toronto have developed a novel AI-driven, microwave-based process to rapidly synthesize perovskite nanocrystals doped with Ytterbium. These nanomaterials emit intense near-infrared light by converting a single high-energy photon into two lower-energy photons (a phenomenon called quantum cutting). This technology enables on-demand tuning of light emission and could lead to more efficient solar panels (by harvesting otherwise wasted UV light) and improved infrared imaging for medical applications.

An integrated Density Functional Theory – Bayesian Optimization strategy efficiently discoveries top-performing MOFs for sustainable carbon capture.

We created a small, automated lab-on-a-chip system combined with machine learning to quickly and efficiently find the best ways to give multiple cancer drugs together or in sequence. This approach helps reduce the amount of testing needed, supports more personalized cancer treatments, and could also be used for other diseases beyond cancer.

We present a versatile and modular Self Driving Lab (SDL) capable of running parallel syntheses, with seamless integration of synthesis, purification, in-line characterization steps, data analysis after characterization, for nano and advanced materials. The SDL successfully synthesizes diverse nanomaterials with different chemistries with excellent reproducibility, demonstrating it’s potential for accelerating materials discovery.

We will share the promising research direction using AI for sustainable 2D materials and energy harvesting materials, which can be implemented in flexible electronics.

We developed a data-driven platform combining high-throughput electrochemistry and machine learning to discover and design molecular additives for interface engineering. Applied to copper electroplating, CO₂ reduction, and H/D separation, our approach reveals structure-function relationships and enables the rational tuning of interfacial properties.

This presentation highlights our recent advancements in multimodal AI and autonomous self-driving laboratories aimed at accelerating research and development in plastics and their processing.

The search for robust spin defects in two-dimensional (2D) transition metal dichalcogenides (TMDs) has emerged as a critical frontier in quantum materials science, driven by their potential as solid-state qubits for quantum sensing and communication. However, the combinatorial complexity of defect configurations and the need for precise electronic and magnetic properties pose significant challenges for traditional trial-and-error approaches. This work presents a synergistic framework combining high-throughput theoretical simulations of X-ray absorption spectroscopy (XAS) and X-ray magnetic circular dichroism (XMCD) with state-of-the-art machine learning (ML) to accelerate the identification of spin-defect candidates in TMDs

We will present new developments in our self-driving laboratory designed for advanced plastics that combine sustainable polymer matrices with cutting-edge functional additives. By automating material processing, specimen fabrication, and characterization, our setup autonomously explores various formulations and produces test specimens for evaluation.

This research introduces a new class of drug-release materials—Therapeutic Coordination Polymers—developed through high-throughput screening and crystal engineering. These materials enable controlled, extended release of common medications like NSAIDs. This approach showcases how rational materials design can improve drug performance through tunable, next-generation pharmaceutical release systems.