This 3-day hands-on workshop The Inverse Design Lab teaches researchers to optimize complex formulations using Bayesian Optimization and Gaussian Processes. Participants will learn to balance trade-offs, like strength vs. conductivity, by leveraging AI to model uncertainty and discover the Pareto Frontier. This hands-on workshop enables efficient experimental design, saving time and resources in material science, chemistry, and engineering.
Aim
AI-powered Bayesian Optimization and Gaussian Processes optimize formulations, balancing properties like strength vs. conductivity and refining designs with fewer experiments.
What Participants Will Learn
Understand Gaussian Processes for small datasets.
Master Multi-Objective Optimization using Bayesian Optimization.
Optimize Experimental Design via the Pareto Frontier.
Implement Active Learning to refine experiments.
Apply AI to enhance experimental outcomes.
Structure
📅 Day 1 — The Mathematics of Uncertainty (Gaussian Processes)
Introduction to Gaussian Processes (GPs) for modeling uncertainty in experimental data.
Practical setup: Building a GP model in Python (BoTorch or GPyTorch) using experimental spreadsheet data (e.g., Composition % vs. Compressive Strength).
Hands-on: Configure and run a GP model using personal experimental data.
Deliverable: A plot visualizing the uncertainty and predictions from the Gaussian Process model.
📅 Day 2 — Multi-Objective Optimization & The Pareto Frontier
Defining Acquisition Functions to guide AI in balancing exploration and exploitation.
Visualizing the Pareto Frontier for trade-offs (e.g., Strength vs. Sorption). Learn how to optimize competing objectives simultaneously.
Hands-on: Run a closed-loop simulation where AI suggests the next 5 experiments to maximize two conflicting variables.
Deliverable: Visualize the Pareto Frontier and interpret the optimal balance between competing properties.
📅 Day 3 — Active Learning for Experimental Design
Integrating human intuition with AI through Human-in-the-loop AI for refining experimental designs.
Hands-on: Create a recommender system that takes previous experiment results and generates the next optimal synthesis recipe.
Deliverable: A Python script that recommends the next experiment based on past results, optimizing for the desired outcomes.
Important Dates
Registration Ends
04:30 PM
Workshop Dates
2026-03-16
05:30 PM
05:30 PM
What You Will Gain
Outcomes
Proficiency in Gaussian Processes for small datasets.
Ability to apply Bayesian Optimization for multi-objective formulations.
Skills in visualizing and optimizing the Pareto Frontier.
Expertise in integrating AI and human intuition for experimental design.
Capability to build AI-driven recommender systems for experiments.
Improved efficiency and decision-making in experimental design using AI.
Who Should Attend
Researchers and R&D professionals optimizing multi-component formulations with multi-objective trade-offs (often N < 50).
PhD/postdoc/faculty/industry scientists in materials, chemical/polymer, civil/energy—incl. composites, coatings/adhesives, membranes, cementitious/grout.
Ideal for teams moving beyond OFAT/limited DoE.
Recommended: Excel/CSV + basic Python/Jupyter (no ML required).
