This year’s conference will be held at the following venues in Toronto. Below is a schedule outline that will be updated shortly.
MaRS Discovery District
Schwartz Reisman Innovation Campus
This year’s conference will be held at the following venues in Toronto. Below is a schedule outline that will be updated shortly.
Join us for breakfast and an introduction to the basics of Self-Driving Labs! Please note, this breakfast has limited capacity and pre-registration is required.
The rise of the Self-Driving Lab (SDL) represents a paradigm shift in R&D, yet the pathway to adoption can seem complex. This session provides a clear and accessible blueprint for getting started, designed for an audience with no prior expertise in automation, robotics, or machine learning. We will introduce the fundamental concepts of an SDL, explaining the interplay of hardware, software, and workflow orchestration through high-level examples of successful implementations. The focus then shifts to a hands-on, strategic planning exercise. Interactive case studies will address real-world problems faced when applying SDL principles to a particular research program. Together, we will walk through critical considerations like scoping capabilities, risks, implementation challenges, and project design, equipping you with the background knowledge and practical insights needed to take the first steps toward building an effective SDL.
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If you're interested in contributing to this workshop, please consider participating in the call for short papers. The top 3 challenge winners will each be awarded $250 CAD.
This workshop will include a panel discussion with leaders from academia and industry, followed by tours and demonstration of the Medicinal Chemistry and Human Organ Mimicry Self-Driving Labs at the Acceleration Consortium. The panel discussion will be focused on how autonomous discovery can open new research directions, how to prepare trainees for labs of the future, and how to address the unique barriers to implementing self-driving labs in a life science context. Whether you're an established researcher in the accelerated discovery community or curious about how self-driving labs could work for your research, join us for a dynamic discussion!
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This workshop, “Self-Driving Lab for All: Build, Automate, Discover,” aims to make self-driving laboratories accessible, affordable, and modular for researchers, educators, and innovators. We will explore how low-cost hardware and open-source software can enable autonomous scientific experimentation for labs of any size. Participants will learn the fundamental concepts of self-driving labs, including hardware, software, and AI-driven optimization techniques. The session will feature live demonstrations of a functional mini self-driving lab, hands-on examples, and interactive discussions on how automation and AI can accelerate discovery and democratize research. No prior experience in automation is required—our goal is to inspire and empower everyone to build and use self-driving labs.
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As a highly interdisciplinary field, the process chemistry industry requires professionals with a broad and versatile skill set. In this session, we discuss what skills are increasingly important for a successful career in process chemistry, particularly in the context of emerging self driving labs and automation technologies that are transforming process development and the chemist’s role. The workshop is conducted in a form of panel discussion involving representative selection of experts from industry and academia invested in the topic. We will share the results of a relevant survey of pioneering stakeholders from pharmaceutical and fine organic chemical process R&D, and the audience will be able to participate in the open discussion via a live poll and a live Q&A session. Additionally, we aim to offer insights on how the industry and academia can join forces to advance process chemistry training for young scientists. Where you are a student, early-career scientist, or established academic/industrial leader, we will aim to provide meaningful insights into process chemistry-related training with a focus on laboratory automation, as well as opportunities to connect with like-minded professionals.
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This workshop will focus on how knowledge graphs can help with materials science and engineering, and specifically focusing on how they can support intelligent autonomous experimentation and research, especially as bridging the gap in cyberphysical systems (e.g., self-driving labs). The session will start with a brief introduction on knowledge graphs (domain-agnostic), followed by some examples directly pertaining to MSE, as well as some hands-on examples for building such knowledge graphs automatically. The session will close with an extended facilitated discussion on open questions that knowledge engineering can help answer.
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