AI Can Suggest an Experiment. Agentic R&D Can Decide What Should Happen Next.
Artificial intelligence is already helping scientists search literature, generate hypotheses and propose formulations. The bigger shift in chemical R&D begins when AI becomes part of the experimental decision loop itself. Instead of manually selecting every next formulation, material combination or processing condition, an agentic system can evaluate previous results, identify which experiment is most valuable to run next and continuously refine the experimental path.
When AI laboratory automation and analytical feedback are connected to this decision layer, workflows can progress toward closed-loop experimentation and self-driving laboratories for chemical R&D, where experimental design, execution, analysis and optimization become increasingly connected.
The challenge is not simply connecting a large language model to laboratory equipment. Chemical R&D involves interacting variables, noisy data, failed experiments, safety limits and scientific context that still require structured decision-making and human oversight.
This advanced training examines how agentic AI, AI agents for chemistry, active learning, Bayesian optimization and laboratory automation can support more adaptive workflows across formulation development, materials discovery and process optimization. The focus is on practical progression from AI-assisted experimentation to increasingly autonomous R&D workflows, including approaches that can be introduced before full laboratory robotics are available.
Why Attend?
Move beyond passive AI assistance
Accelerate formulation optimization cycles
Select higher-value next experiments
Connect experimental data with decisions
Understand self-driving laboratory architecture
Identify realistic automation opportunities
Who Should Attend?
This advanced training is particularly relevant for:
Chemical and materials R&D scientists
Formulation and product development specialists
Laboratory automation and digital R&D teams
Data scientists supporting experimental R&D
High-throughput experimentation specialists
R&D managers developing AI-enabled laboratory strategies
Training Outline
Agentic AI in Chemical and Materials R&D
From AI Assistants to Autonomous Experimental Workflows
AI Agents for Chemistry and Experimental Planning
Bayesian Optimization and Active Learning
Formulation Optimization Across Multiple Variables
AI Laboratory Automation, Analytics and Experimental Feedback
Building Closed-Loop Experimental Workflows
Data Quality, Failed Experiments and Scientific Context
Human Oversight, Safety and Experimental Guardrails
Practical Roadmap Toward Self-Driving R&D
Case Studies
Expert Q&A Session
Move From AI-Generated Answers to AI-Driven Experimental Decisions.
Build a practical framework for using agentic AI, autonomous experimentation, self-driving laboratories and closed-loop formulation optimization to make chemical R&D faster, more adaptive and more experimentally productive. Register Now.
