The Inverse Design Lab: Multi-Objective Bayesian Optimization for Complex Formulations

AI-powered Bayesian Optimization and Gaussian Processes optimize formulations, balancing properties like strength vs. conductivity and refining designs with fewer experiments.

• 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.

📅 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.

• Doctoral Scholars & Researchers: PhD candidates seeking to integrate computational workflows into their molecular research.
• Postdoctoral Fellows: Early-career scientists aiming to enhance their data-driven publication profile.
• University Faculty: Professors and HODs interested in modern bioinformatics pedagogy and tool mastery.
• Industry Scientists: R&D professionals from the Biotechnology and Pharmaceutical sectors transitioning to genomic-driven discovery.